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Question 173: Retain Every Likelihood Maximizer before Discussing Uniqueness On this page Original question and givens Definition and scope Step-by-step derivation Reverse checks Result This question review and source binding Return to catalogue page 1
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This teaching derivation has second-context AI mathematical and bilingual semantic review, limited to the model and assumptions stated in this question. It has no human review or new Lean proof and does not promote the original question or MKO to Canonical truth.
P250-0173 · q-20261015-advance-20261116-r04-finite-maximum-likelihood-estimator-0048
Original question and givens Use complete theta labels=[a22, b22], observations=[x22, y22] and aligned exact probability rows=[[[3, 4], [1, 4]], [[1, 4], [3, 4]]]. Each theta row is nonnegative and sums to1. Sort labels with their associated rows/columns, never independently. Observed label=x22. Give (observed, likelihood_rows, maximum_likelihood, argmax_parameters, unique_argmax, certificate).
G001 — Use complete theta labels=[a22, b22], observations=[x22, y22] and aligned exact probability rows=[[[3, 4], [1, 4]], [[1, 4], [3, 4]]]. Each theta row is nonnegative and sums to1. Sort labels with their associated rows/columns, never independently. Observed label=x22. Give (observed, likelihood_rows, maximum_likelihood, argmax_parameters, unique_argmax, certificate).Definition and scope Use complete finite parameter and observation carriers. Fix the observed column, retain the full argmax, and introduce no prior.
Step-by-step derivation S001 · implies Before
Θ=[a22, b22]; Ω=[x22, y22] After
observed=x22, column=0 Conditions: none
Fix the observed outcome and compare its one probability column across parameter values.
Step identifiers and dependencies Rule: fixed_observation · Uses: G001
S002 · calculation Before
P_a22(Ω) := sum(3/4, 1/4) After
P_a22(Ω) = 1 Conditions: none
First check that this parameter's row remains a normalized probability law.
Step identifiers and dependencies Rule: exact_rational_arithmetic · Uses: G001, S001
S003 · implies Before
row for a22=[3/4, 1/4] After
L(a22;x22)=P_a22(x22)=3/4 Conditions: none
Take the probability at the observed label in this row, without introducing a prior.
Step identifiers and dependencies Rule: likelihood_lookup · Uses: G001, S002
S004 · calculation Before
P_b22(Ω) := sum(1/4, 3/4) After
P_b22(Ω) = 1 Conditions: none
First check that this parameter's row remains a normalized probability law.
Step identifiers and dependencies Rule: exact_rational_arithmetic · Uses: G001, S003
S005 · implies Before
row for b22=[1/4, 3/4] After
L(b22;x22)=P_b22(x22)=1/4 Conditions: none
Take the probability at the observed label in this row, without introducing a prior.
Step identifiers and dependencies Rule: likelihood_lookup · Uses: G001, S004
S006 · implies Before
L(a22;x22)=3/4, maximum=3/4 After
a22 ∈ argmax: Yes Conditions: none
Include every parameter that attains the maximum, including ties after the first maximizer.
Step identifiers and dependencies Rule: argmax_membership · Uses: G001, S005
S007 · implies Before
L(b22;x22)=1/4, maximum=3/4 After
b22 ∈ argmax: No Conditions: none
Include every parameter that attains the maximum, including ties after the first maximizer.
Step identifiers and dependencies Rule: argmax_membership · Uses: G001, S006
S008 · implies Before
argmax=[a22] After
unique maximizing parameter: Yes Conditions: none
Only a complete argmax with exactly one element has a unique maximizing parameter.
Step identifiers and dependencies Rule: cardinality_one · Uses: G001, S007
S009 · implies Before
finite normalized laws After
MLE is not a posterior or an automatic quality certificate Conditions: none
If all likelihoods at an observation are zero, every parameter still maximizes at 0; this computation does not imply unbiasedness or consistency.
Step identifiers and dependencies Rule: scope_boundary · Uses: G001, S008
Reverse checks S009 · premise_and_result_audit — Do not confuse a null observation column with an invalid all-zero probability-law row.S008 · premise_and_result_audit — A fixed tie-breaker can select one output without making a multi-element argmax a singleton.S007 · premise_and_result_audit — The maximum is a number; argmax is a set of parameters.S006 · premise_and_result_audit — The maximum is a number; argmax is a set of parameters.S005 · premise_and_result_audit — The value remains bound to the same parameter and observation.S004 · premise_and_result_audit — Recompute P_b22(Ω) with exact rational arithmetic, not rounded decimals.S003 · premise_and_result_audit — The value remains bound to the same parameter and observation.S002 · premise_and_result_audit — Recompute P_a22(Ω) with exact rational arithmetic, not rounded decimals.S001 · premise_and_result_audit — Do not confuse other observations in a row with other parameter values.Result (x22, [[a22, 3/4], [b22, 1/4]], 3/4, [a22], Yes, MLE_argmax_need_not_be_unique) The maximum likelihood is 3/4; the complete argmax is [a22].
This question review and source binding Review date: 2026-10-24
Original questions, answers, step expressions, numeric ASTs and historical records are retained. Source candidate labels remain historical and are not overwritten as formal proof.
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