本批已通過 Schema、去重、來源快照與獨立答案重算；仍未標記為人工審定。
本題庫已有對應的中英逐步詳解。原題的 candidate 與英文 ai_candidate 狀態保留；AI 覆核、有限計算與 Lean 形式證明分開標示。
batch-20261014-advance-20261102-r06-bayes-theorem-foundations · 2026-10-14 · 100 題
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,8],[1,8],[1,8],[5,8]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(1/4, 1/2, 1/4, 1/8, 1/2, not_causal)
Wave5第二層核對=(1/4, 1/2, 1/4, 1/8, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,9],[1,9],[1,9],[6,9]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(2/9, 1/2, 2/9, 1/9, 1/2, not_causal)
Wave5第二層核對=(2/9, 1/2, 2/9, 1/9, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,10],[1,10],[1,10],[7,10]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(1/5, 1/2, 1/5, 1/10, 1/2, not_causal)
Wave5第二層核對=(1/5, 1/2, 1/5, 1/10, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,11],[1,11],[1,11],[8,11]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(2/11, 1/2, 2/11, 1/11, 1/2, not_causal)
Wave5第二層核對=(2/11, 1/2, 2/11, 1/11, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,12],[1,12],[1,12],[9,12]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(1/6, 1/2, 1/6, 1/12, 1/2, not_causal)
Wave5第二層核對=(1/6, 1/2, 1/6, 1/12, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,13],[1,13],[1,13],[10,13]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(2/13, 1/2, 2/13, 1/13, 1/2, not_causal)
Wave5第二層核對=(2/13, 1/2, 2/13, 1/13, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,14],[1,14],[1,14],[11,14]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(1/7, 1/2, 1/7, 1/14, 1/2, not_causal)
Wave5第二層核對=(1/7, 1/2, 1/7, 1/14, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,15],[1,15],[1,15],[12,15]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(2/15, 1/2, 2/15, 1/15, 1/2, not_causal)
Wave5第二層核對=(2/15, 1/2, 2/15, 1/15, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,16],[1,16],[1,16],[13,16]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(1/8, 1/2, 1/8, 1/16, 1/2, not_causal)
Wave5第二層核對=(1/8, 1/2, 1/8, 1/16, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,17],[1,17],[1,17],[14,17]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(2/17, 1/2, 2/17, 1/17, 1/2, not_causal)
Wave5第二層核對=(2/17, 1/2, 2/17, 1/17, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,18],[1,18],[1,18],[15,18]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(1/9, 1/2, 1/9, 1/18, 1/2, not_causal)
Wave5第二層核對=(1/9, 1/2, 1/9, 1/18, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,19],[1,19],[1,19],[16,19]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(2/19, 1/2, 2/19, 1/19, 1/2, not_causal)
Wave5第二層核對=(2/19, 1/2, 2/19, 1/19, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,20],[1,20],[1,20],[17,20]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(1/10, 1/2, 1/10, 1/20, 1/2, not_causal)
Wave5第二層核對=(1/10, 1/2, 1/10, 1/20, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,21],[1,21],[1,21],[18,21]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(2/21, 1/2, 2/21, 1/21, 1/2, not_causal)
Wave5第二層核對=(2/21, 1/2, 2/21, 1/21, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,22],[1,22],[1,22],[19,22]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(1/11, 1/2, 1/11, 1/22, 1/2, not_causal)
Wave5第二層核對=(1/11, 1/2, 1/11, 1/22, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,23],[1,23],[1,23],[20,23]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(2/23, 1/2, 2/23, 1/23, 1/2, not_causal)
Wave5第二層核對=(2/23, 1/2, 2/23, 1/23, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,24],[1,24],[1,24],[21,24]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(1/12, 1/2, 1/12, 1/24, 1/2, not_causal)
Wave5第二層核對=(1/12, 1/2, 1/12, 1/24, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,25],[1,25],[1,25],[22,25]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(2/25, 1/2, 2/25, 1/25, 1/2, not_causal)
Wave5第二層核對=(2/25, 1/2, 2/25, 1/25, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,26],[1,26],[1,26],[23,26]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(1/13, 1/2, 1/13, 1/26, 1/2, not_causal)
Wave5第二層核對=(1/13, 1/2, 1/13, 1/26, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,27],[1,27],[1,27],[24,27]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(2/27, 1/2, 2/27, 1/27, 1/2, not_causal)
Wave5第二層核對=(2/27, 1/2, 2/27, 1/27, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,28],[1,28],[1,28],[25,28]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(1/14, 1/2, 1/14, 1/28, 1/2, not_causal)
Wave5第二層核對=(1/14, 1/2, 1/14, 1/28, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,29],[1,29],[1,29],[26,29]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(2/29, 1/2, 2/29, 1/29, 1/2, not_causal)
Wave5第二層核對=(2/29, 1/2, 2/29, 1/29, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,30],[1,30],[1,30],[27,30]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(1/15, 1/2, 1/15, 1/30, 1/2, not_causal)
Wave5第二層核對=(1/15, 1/2, 1/15, 1/30, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,31],[1,31],[1,31],[28,31]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(2/31, 1/2, 2/31, 1/31, 1/2, not_causal)
Wave5第二層核對=(2/31, 1/2, 2/31, 1/31, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-two-event-reversal；資料={"masses":[[1,32],[1,32],[1,32],[29,32]],"hypothesis":[0,1],"evidence":[0,2]}。請依 [prior,likelihood,evidence_mass,joint,posterior,boundary] 欄序回答。
(1/16, 1/2, 1/16, 1/32, 1/2, not_causal)
Wave5第二層核對=(1/16, 1/2, 1/16, 1/32, 1/2, not_causal)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-two-event-reversal · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,8],[1,8],[1,8],[5,8]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":0}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(0, 1/4, 1/2, 1/4, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(0, 1/4, 1/2, 1/4, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,9],[1,9],[1,9],[6,9]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":1}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(1, 7/9, 1/7, 2/9, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(1, 7/9, 1/7, 2/9, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,10],[1,10],[1,10],[7,10]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":0}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(0, 1/5, 1/2, 1/5, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(0, 1/5, 1/2, 1/5, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,11],[1,11],[1,11],[8,11]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":1}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(1, 9/11, 1/9, 2/11, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(1, 9/11, 1/9, 2/11, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,12],[1,12],[1,12],[9,12]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":0}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(0, 1/6, 1/2, 1/6, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(0, 1/6, 1/2, 1/6, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,13],[1,13],[1,13],[10,13]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":1}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(1, 11/13, 1/11, 2/13, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(1, 11/13, 1/11, 2/13, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,14],[1,14],[1,14],[11,14]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":0}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(0, 1/7, 1/2, 1/7, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(0, 1/7, 1/2, 1/7, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,15],[1,15],[1,15],[12,15]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":1}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(1, 13/15, 1/13, 2/15, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(1, 13/15, 1/13, 2/15, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,16],[1,16],[1,16],[13,16]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":0}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(0, 1/8, 1/2, 1/8, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(0, 1/8, 1/2, 1/8, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,17],[1,17],[1,17],[14,17]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":1}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(1, 15/17, 1/15, 2/17, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(1, 15/17, 1/15, 2/17, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,18],[1,18],[1,18],[15,18]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":0}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(0, 1/9, 1/2, 1/9, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(0, 1/9, 1/2, 1/9, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,19],[1,19],[1,19],[16,19]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":1}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(1, 17/19, 1/17, 2/19, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(1, 17/19, 1/17, 2/19, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,20],[1,20],[1,20],[17,20]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":0}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(0, 1/10, 1/2, 1/10, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(0, 1/10, 1/2, 1/10, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,21],[1,21],[1,21],[18,21]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":1}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(1, 19/21, 1/19, 2/21, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(1, 19/21, 1/19, 2/21, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,22],[1,22],[1,22],[19,22]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":0}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(0, 1/11, 1/2, 1/11, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(0, 1/11, 1/2, 1/11, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,23],[1,23],[1,23],[20,23]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":1}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(1, 21/23, 1/21, 2/23, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(1, 21/23, 1/21, 2/23, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,24],[1,24],[1,24],[21,24]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":0}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(0, 1/12, 1/2, 1/12, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(0, 1/12, 1/2, 1/12, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,25],[1,25],[1,25],[22,25]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":1}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(1, 23/25, 1/23, 2/25, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(1, 23/25, 1/23, 2/25, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,26],[1,26],[1,26],[23,26]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":0}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(0, 1/13, 1/2, 1/13, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(0, 1/13, 1/2, 1/13, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,27],[1,27],[1,27],[24,27]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":1}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(1, 25/27, 1/25, 2/27, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(1, 25/27, 1/25, 2/27, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,28],[1,28],[1,28],[25,28]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":0}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(0, 1/14, 1/2, 1/14, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(0, 1/14, 1/2, 1/14, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,29],[1,29],[1,29],[26,29]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":1}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(1, 27/29, 1/27, 2/29, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(1, 27/29, 1/27, 2/29, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,30],[1,30],[1,30],[27,30]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":0}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(0, 1/15, 1/2, 1/15, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(0, 1/15, 1/2, 1/15, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,31],[1,31],[1,31],[28,31]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":1}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(1, 29/31, 1/29, 2/31, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(1, 29/31, 1/29, 2/31, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-partition-posterior；資料={"masses":[[1,32],[1,32],[1,32],[29,32]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2],"target_index":0}。請依 [target_index,prior,likelihood,evidence_mass,posterior,partition_certificate] 欄序回答。
(0, 1/16, 1/2, 1/16, 1/2, partition_pairwise_disjoint_exhaustive)
Wave5第二層核對=(0, 1/16, 1/2, 1/16, 1/2, partition_pairwise_disjoint_exhaustive)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-partition-posterior · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,8],[1,8],[1,8],[5,8]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([1/4, 3/4], [1/2, 1/6], [1/8, 1/8], 1/4, 1/4, 是)
Wave5第二層核對=([1/4, 3/4], [1/2, 1/6], [1/8, 1/8], 1/4, 1/4, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,9],[1,9],[1,9],[6,9]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([2/9, 7/9], [1/2, 1/7], [1/9, 1/9], 2/9, 2/9, 是)
Wave5第二層核對=([2/9, 7/9], [1/2, 1/7], [1/9, 1/9], 2/9, 2/9, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,10],[1,10],[1,10],[7,10]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([1/5, 4/5], [1/2, 1/8], [1/10, 1/10], 1/5, 1/5, 是)
Wave5第二層核對=([1/5, 4/5], [1/2, 1/8], [1/10, 1/10], 1/5, 1/5, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,11],[1,11],[1,11],[8,11]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([2/11, 9/11], [1/2, 1/9], [1/11, 1/11], 2/11, 2/11, 是)
Wave5第二層核對=([2/11, 9/11], [1/2, 1/9], [1/11, 1/11], 2/11, 2/11, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,12],[1,12],[1,12],[9,12]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([1/6, 5/6], [1/2, 1/10], [1/12, 1/12], 1/6, 1/6, 是)
Wave5第二層核對=([1/6, 5/6], [1/2, 1/10], [1/12, 1/12], 1/6, 1/6, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,13],[1,13],[1,13],[10,13]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([2/13, 11/13], [1/2, 1/11], [1/13, 1/13], 2/13, 2/13, 是)
Wave5第二層核對=([2/13, 11/13], [1/2, 1/11], [1/13, 1/13], 2/13, 2/13, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,14],[1,14],[1,14],[11,14]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([1/7, 6/7], [1/2, 1/12], [1/14, 1/14], 1/7, 1/7, 是)
Wave5第二層核對=([1/7, 6/7], [1/2, 1/12], [1/14, 1/14], 1/7, 1/7, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,15],[1,15],[1,15],[12,15]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([2/15, 13/15], [1/2, 1/13], [1/15, 1/15], 2/15, 2/15, 是)
Wave5第二層核對=([2/15, 13/15], [1/2, 1/13], [1/15, 1/15], 2/15, 2/15, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,16],[1,16],[1,16],[13,16]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([1/8, 7/8], [1/2, 1/14], [1/16, 1/16], 1/8, 1/8, 是)
Wave5第二層核對=([1/8, 7/8], [1/2, 1/14], [1/16, 1/16], 1/8, 1/8, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,17],[1,17],[1,17],[14,17]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([2/17, 15/17], [1/2, 1/15], [1/17, 1/17], 2/17, 2/17, 是)
Wave5第二層核對=([2/17, 15/17], [1/2, 1/15], [1/17, 1/17], 2/17, 2/17, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,18],[1,18],[1,18],[15,18]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([1/9, 8/9], [1/2, 1/16], [1/18, 1/18], 1/9, 1/9, 是)
Wave5第二層核對=([1/9, 8/9], [1/2, 1/16], [1/18, 1/18], 1/9, 1/9, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,19],[1,19],[1,19],[16,19]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([2/19, 17/19], [1/2, 1/17], [1/19, 1/19], 2/19, 2/19, 是)
Wave5第二層核對=([2/19, 17/19], [1/2, 1/17], [1/19, 1/19], 2/19, 2/19, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,20],[1,20],[1,20],[17,20]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([1/10, 9/10], [1/2, 1/18], [1/20, 1/20], 1/10, 1/10, 是)
Wave5第二層核對=([1/10, 9/10], [1/2, 1/18], [1/20, 1/20], 1/10, 1/10, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,21],[1,21],[1,21],[18,21]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([2/21, 19/21], [1/2, 1/19], [1/21, 1/21], 2/21, 2/21, 是)
Wave5第二層核對=([2/21, 19/21], [1/2, 1/19], [1/21, 1/21], 2/21, 2/21, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,22],[1,22],[1,22],[19,22]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([1/11, 10/11], [1/2, 1/20], [1/22, 1/22], 1/11, 1/11, 是)
Wave5第二層核對=([1/11, 10/11], [1/2, 1/20], [1/22, 1/22], 1/11, 1/11, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,23],[1,23],[1,23],[20,23]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([2/23, 21/23], [1/2, 1/21], [1/23, 1/23], 2/23, 2/23, 是)
Wave5第二層核對=([2/23, 21/23], [1/2, 1/21], [1/23, 1/23], 2/23, 2/23, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,24],[1,24],[1,24],[21,24]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([1/12, 11/12], [1/2, 1/22], [1/24, 1/24], 1/12, 1/12, 是)
Wave5第二層核對=([1/12, 11/12], [1/2, 1/22], [1/24, 1/24], 1/12, 1/12, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,25],[1,25],[1,25],[22,25]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([2/25, 23/25], [1/2, 1/23], [1/25, 1/25], 2/25, 2/25, 是)
Wave5第二層核對=([2/25, 23/25], [1/2, 1/23], [1/25, 1/25], 2/25, 2/25, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,26],[1,26],[1,26],[23,26]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([1/13, 12/13], [1/2, 1/24], [1/26, 1/26], 1/13, 1/13, 是)
Wave5第二層核對=([1/13, 12/13], [1/2, 1/24], [1/26, 1/26], 1/13, 1/13, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,27],[1,27],[1,27],[24,27]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([2/27, 25/27], [1/2, 1/25], [1/27, 1/27], 2/27, 2/27, 是)
Wave5第二層核對=([2/27, 25/27], [1/2, 1/25], [1/27, 1/27], 2/27, 2/27, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,28],[1,28],[1,28],[25,28]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([1/14, 13/14], [1/2, 1/26], [1/28, 1/28], 1/14, 1/14, 是)
Wave5第二層核對=([1/14, 13/14], [1/2, 1/26], [1/28, 1/28], 1/14, 1/14, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,29],[1,29],[1,29],[26,29]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([2/29, 27/29], [1/2, 1/27], [1/29, 1/29], 2/29, 2/29, 是)
Wave5第二層核對=([2/29, 27/29], [1/2, 1/27], [1/29, 1/29], 2/29, 2/29, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,30],[1,30],[1,30],[27,30]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([1/15, 14/15], [1/2, 1/28], [1/30, 1/30], 1/15, 1/15, 是)
Wave5第二層核對=([1/15, 14/15], [1/2, 1/28], [1/30, 1/30], 1/15, 1/15, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,31],[1,31],[1,31],[28,31]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([2/31, 29/31], [1/2, 1/29], [1/31, 1/31], 2/31, 2/31, 是)
Wave5第二層核對=([2/31, 29/31], [1/2, 1/29], [1/31, 1/31], 2/31, 2/31, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-total-evidence；資料={"masses":[[1,32],[1,32],[1,32],[29,32]],"hypotheses":[[0,1],[2,3]],"evidence":[0,2]}。請依 [priors,likelihoods,joints,total,evidence_mass,equal] 欄序回答。
([1/16, 15/16], [1/2, 1/30], [1/32, 1/32], 1/16, 1/16, 是)
Wave5第二層核對=([1/16, 15/16], [1/2, 1/30], [1/32, 1/32], 1/16, 1/16, 是)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-total-evidence · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,8],[1,8],[1,8],[5,8]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,9],[1,9],[1,9],[6,9]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,10],[1,10],[1,10],[7,10]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,11],[1,11],[1,11],[8,11]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,12],[1,12],[1,12],[9,12]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,13],[1,13],[1,13],[10,13]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,14],[1,14],[1,14],[11,14]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,15],[1,15],[1,15],[12,15]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,16],[1,16],[1,16],[13,16]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,17],[1,17],[1,17],[14,17]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,18],[1,18],[1,18],[15,18]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,19],[1,19],[1,19],[16,19]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,20],[1,20],[1,20],[17,20]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,21],[1,21],[1,21],[18,21]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,22],[1,22],[1,22],[19,22]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,23],[1,23],[1,23],[20,23]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,24],[1,24],[1,24],[21,24]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,25],[1,25],[1,25],[22,25]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,26],[1,26],[1,26],[23,26]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,27],[1,27],[1,27],[24,27]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,28],[1,28],[1,28],[25,28]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,29],[1,29],[1,29],[26,29]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,30],[1,30],[1,30],[27,30]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,31],[1,31],[1,31],[28,31]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate
完整有限原子law、事件與正conditioning denominator。操作=bayes-zero-denominator-boundary；資料={"masses":[[1,32],[1,32],[1,32],[29,32]],"hypothesis":[0,1],"evidence":[]}。請依 [evidence_mass,conditional_value,defined,boundary] 欄序回答。
(0, undefined, 否, conditional_probability_requires_positive_denominator)
Wave5第二層核對=(0, undefined, 否, conditional_probability_requires_positive_denominator)；依完整明示資料重算；partition須pairwise disjoint且exhaustive並由total probability正規化；不可漏prior、反轉零分母或把Bayes當causation。
貝氏定理 · qf-mod33-bayes-zero-denominator-boundary · candidate