Variable Elimination (VE) is the most basic one of many Bayesian network inference algorithms. The speed and complexity reasoning mainly depend on order elimination. Finding optimal elimination a Nondeterministic Polynomial Hard (NP-Hard) problem, which often solved by heuristic search in practice. In to improve variable method, minimum, maximum potential, minimum missing edge added methods are...

In this paper, we propose a method for constructing sharedstate triphone HMMs (SST-HMMs) within a practical Bayesian framework. In our method, Bayesian model selection criterion is derived for SST-HMM based on the Variational Bayesian approach. The appropriate phonetic decision tree structure of SST-HMM is found by using the criterion according to a given data set. This criterion, unlike the co...

In this paper, we propose a method for constructing sharedstate triphone HMMs (SST-HMMs) within a practical Bayesian framework. In our method, Bayesian model selection criterion is derived for SST-HMM based on the Variational Bayesian approach. The appropriate phonetic decision tree structure of SST-HMM is found by using the criterion according to a given data set. This criterion, unlike the co...

We have developed a frequentist approach for model selection which determines the consistency between any cosmological and data using distribution of likelihoods from iterative smoothing method. Using this approach, we shown how confidently can conclude whether support given without comparison to different one. In current work, compare our with conventional Bayesian based on estimation evidence...