application of backward probability method in pollutant source tracking in non-uniform flow riversalireza ghanem.sc. student of water structures, tarbiat modares university, tehranmehdi mazaheri assistant prof., department of water structures, tarbiat modares university, tehranjamal mohammad vali samaniprofessor, department of water structures, tarbiat modares university, tehranintroductionriv...

It has been discovered that there are two stem cell groups in the intestinal crypts: central stem cells (CeSCs), which are at the very bottom of the crypt, and border stem cells (BSCs), which are located between CeSCs and transit amplifying cells (TAs). Moreover, backward cell migration from BSCs to CeSCs has been observed. Recently, a bi-compartmental stochastic model, which includes CeSCs and...

This paper describes how to specify probability models for data analysis via a backward induction procedure. The new approach yields coherent, priorfree uncertainty assessment. The backward induction approach is first demonstrated on two familiar models — the Bernoulli distribution and the Gaussian distribution — to compare the resulting specifications to their standard counterparts arising as ...

In the paper, the method of short word deletion errors correction in automatic speech recognition is described. Short word deletion errors appear to be a frequent error type in Polish speech recognition. The proposed speech recognition process consists of two stages. At the first stage the utterance is recognized by a typical speech recognizer based on forward bigram language model. At the seco...

In the POS tagging task, there are two kinds of statistical models: one is generative model, such as the HMM, the others are discriminative models, such as the Maximum Entropy Model (MEM). POS multi-tagging decoding method includes the N-best paths method and forward-backward method. In this paper, we use the forward-backward decoding method based on a combined model of HMM and MEM. If P(t) is ...

This paper proposes a State-Correlated Duration model for HMM-based speech synthesis system. It uses an improved forward-backward algorithm to estimate the state-duration transition probability between the neighboring states. In the synthesis part, we determine the state duration taking account of the state-duration transition probability. Experiment results show that the speech we synthesized ...

Proper consistency is defined by the properties that each player takes all opponent strategies into account (is cautious) and deems one opponent strategy to be infinitely more likely than another if the opponent prefers the one to the other (respects preferences). When there is common certain belief of proper consistency, a most preferred strategy is properly rationalizable. Any strategy used w...

A hidden Markov model (HMM) defines a joint probability distribution of a series of observations xt and hidden states zt for t = 1, . . . , T . We use x1:T and z1:T to refer to the full sequence of observations and states respectively. In a HMM the prior on the state sequence is assumed to satisfy the Markov property, which is to say that the probability of each state zt depends only on the pre...