{"schema_version":"ocme-site-results-v1","locale":"en","page_type":"derivations","facts":{"questions":36000,"reviewed":36000,"display_candidates":0,"knowledge":302,"guides":299,"paths":59,"batches":360,"families":1441,"atlas":302,"core":80,"evidence":9,"formal_evidence":5,"formal_subjects":4,"lean_files":6,"lean_toolchain":"leanprover/lean4:v4.30.0","question_state":"candidate","english_state":"ai_candidate","question_Lean":"NOT_RUN","canonical_truth_promotion":false},"filters":{"q":"mko-discrete-stochastic-process"},"kind":"guides","total":1,"pages":1,"page":1,"entries":[{"id":"derivation-mko-discrete-stochastic-process-01","title":{"zh-Hant":"離散隨機過程：由完整路徑分布導出邊際與兩時刻聯合分布，並檢驗 Markov 性與平穩性","en":"Discrete-time stochastic process: marginals and two-time joint laws from a complete path distribution, with Markov and stationarity checks"},"summary":{"zh-Hant":"時域 {0,1,2}、狀態 {0,1}。完整路徑支撐與精確 PMF 為 (0,0,0):1/4、(0,1,1):1/4、(1,0,1):1/8、(1,1,0):1/8、(1,1,1):1/4。(1) 核對支撐與 PMF，建立機率空間並說明 X_0、X_1、X_2 是同一空間上的隨機變數；(2) 求三個時刻的邊際分布與 (X_1,X_2) 的聯合分布；(3) 證明這個過程不平穩、不具 Markov 性；(4) 構造一個邊際相同但聯合分布不同的路徑律，說明邊際表不等於完整過程。","en":"Horizon {0,1,2}, states {0,1}. The complete path support with exact PMF is (0,0,0):1/4, (0,1,1):1/4, (1,0,1):1/8, (1,1,0):1/8, (1,1,1):1/4. (1) Check the support and the PMF, build the probability space and explain why X_0, X_1, X_2 are random variables on the same space; (2) find the marginal law at each time and the joint law of (X_1,X_2); (3) prove that this process is neither stationary nor Markov; (4) construct a path law with the same marginals but a different joint law, showing that the marginal tables are not the whole process."},"mko_ids":["mko-discrete-stochastic-process"]}],"title":"Concept derivation guides"}