This paper proposes a deterministic annealing based training algorithm for Bayesian speech recognition. The Bayesian method is a statistical technique for estimating reliable predictive distributions by marginalizing model parameters. However, the local maxima problem in the Bayesian method is more serious than in the ML-based approach, because the Bayesian method treats not only state sequence...

In this article, we propose a new method for selecting level dependent threshold in wavelet shrinkage using the empirical Bayes framework. We employ both Bayesian and frequentist testing hypothesis instead of point estimation method. The best test yields the best prior and hence the more appropriate wavelet thresholds. The standard model functions are used to illustrate the performance of the p...

We propose an information-theoretic approach for predictive modeling with Bayesian networks. Our approach is based on the minimax optimal Normalized Maximum Likelihood (NML) distribution, motivated by the MDL principle. In particular, we present a parameter learning method which, together with a previously introduced NML-based model selection criterion, provides a way to construct highly predic...

This paper suggests one method to process fMRI time series based on Bayesian inference for group analysis. The method is based on Bayesian inference to divide group into multilevel by session, subject and group levels. It compares covariance to select prior to reinforce posterior probability in group analysis. At the same time it combines classical statistics, i.e., t-statistics to obtain voxel...

In this paper we address some of the concerns raised by Butterworth and Punt (1994) in SC/46/AS2 regarding the Bayesian synthesis method. We argue that the sensitivity of the Bayesian synthesis approach is similar to that of alternative methods, and that the ease with which sensitivity can be detected is one of the advantages of the method. We also note that some of the examples in SC/46/AS2 us...

1. Bayesian Statistical Method The Bayesian Motif Clustering (BMC) algorithm proposed in the main article is based on an explicit statistical model that describes the relationship between the observed motifs and the putative regulons (clusters) and a Markov chain Monte Carlo computational method. We describe first the general Bayesian inference procedure and then its detailed implementation for...

In this paper, we evaluate the Analysis of Competing Hypotheses (ACH) method using a normative Bayesian probabilistic framework. We describe the ACH method and present an example of how to use it to structure an analytic problem. We then show how to represent the same analytic problem using Bayesian networks and compare the result with that using the ACH method. We discuss how Bayesian networks...

here in, an application of a new seismic inversion algorithm in one of iran’s oilfields is described. stochastic (geostatistical) seismic inversion, as a complementary method to deterministic inversion, is perceived as contribution combination of geostatistics and seismic inversion algorithm. this method integrates information from different data sources with different scales, as prior informat...

Educators and psychologists have debated on the efficacy of personalizing teaching methods according to learning style [1-3]. Recently, the styles are obtained from learning models, and it has been shown that utilizing those styles leverages educational effect. However, the researcher uses only two features which are the classification accuracy of human and response time, and it has been sugges...