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Update of the model probability.
(5) Update of the model probability .
where pAM is the acoustic model probability, PLM is the language model probability, and α is the language model scale factor.
The results and the limitations of the data model probability are discussed critically.
Section 2 introduces the interacting multiple model probability data association algorithm.
Thirdly, BMA provides the best performing model in terms of its posterior model probability (PMP).
The posterior model probability (PMP) can be used to derive the structural, model dependent uncertainty.
A discrimination criterion is developed based on posterior model probability that directly uses data to evaluate model importance.
The marginal likelihoods can be combined with a prior probability over models, $P(M_{i})$, to derive the so-called posterior model probability, using Bayes' theorem.
To evaluate the posterior model probability the BMA uses the Bayesian Information Criteria (BIC) to approximate the Bayes factors that are needed to compute the posterior model probability [60-62] [60-62]
The IMM algorithm consists of four steps: input interaction, model filtering, model probability update, and output combination.
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