context-dependent modeling is a well-known approach to increase modeling accuracy in continuous speech recognition. the most common way to implement this approach is via triphone modeling. nevertheless, the large number of such models results in several problems in model training, whilst the robust training of such models is often hardly obtained. one approach to solve this problem is via param...

A more realistic way to describe a model is the use of intervals which contain required values parameters. In practice we estimate parameters from set data and it natural that they will be in confidence intervals. present study, study Non-Homogeneous Markov Systems (NHMS) processes for basic are We call such Set (NHMSS). First relative expected population structure memberships prove under certa...

Fractal stochastic processes are examples of semi-Markov processes where the signal behaviour is a function of the prefiltering bandwidth. In this paper we develop schemes for estimating such fractal models when they are hidden (imbedded) in noise. We reformulate this hidden fractal model (H FM) problem in the scalar case as a higher order scalar or first order 2-vector homogeneous hidden Marko...

Six topologies of dynamic Bayesian Networks are evaluated for predicting the future user events: (1) Markov Chain of order 1, (2) Hidden Markov Model, (3) autoregressive Hidden Markov Model, (4) factorial Hidden Markov Model, (5) simple hierarchical Hidden Markov Model and (6) tree structured Hidden Markov Model. Goal of the investigation is to evaluate, which of these models has the best fit f...

Motivation DNA methylation is an important epigenetic mechanism in gene regulation and the detection of differentially methylated regions (DMRs) is enthralling for many disease studies. There are several aspects that we can improve over existing DMR detection methods: (i) methylation statuses of nearby CpG sites are highly correlated, but this fact has seldom been modelled rigorously due to the...