A profile hidden Markov model (PHMM) is widely used in assigning protein sequences to protein families. In this model, the hidden states only depend on the previous hidden state and observations are independent given hidden states. In other words, in the PHMM, only the information of the left side of a hidden state is considered. However, it makes sense that considering the information of the b...

a profile hidden markov model (phmm) is widely used in assigning protein sequences to protein families. in this model, the hidden states only depend on the previous hidden state and observations are independent given hidden states. in other words, in the phmm, only the information of the left side of a hidden state is considered. however, it makes sense that considering the information of the b...

The parameters of a Hidden Markov Model (HMM) are transition and emission probabilities‎. ‎Both can be estimated using the Baum-Welch algorithm‎. ‎The process of discovering the sequence of hidden states‎, ‎given the sequence of observations‎, ‎is performed by the Viterbi algorithm‎. ‎In both Baum-Welch and Viterbi algorithms‎, ‎it is assumed that...

The Baum-Welch algorithm is a technique for the maximum likelihood parameter estimation of probabilistic functions of Markov processes. We apply this technique to nonstationary Markov processes and explore a relationship between the Baum-Welch algorithm and the BCJR algorithm. Furthermore, we apply the Baum-Welch algorithm to two nonstationary Markov processes and obtain the turbo decoding algo...

We propose a new method for the estimation of parameters of hidden diffusion processes. Based on parametrization of the transition matrix, the Baum-Welch algorithm is improved. The algorithm is compared to the particle filter in application to the noisy periodic systems. It is shown that the modified Baum-Welch algorithm is capable of estimating the system parameters with better accuracy than p...

In this paper we compare the performance of acoustic HMMs obtained through Viterbi training with that of acoustic HMMs obtained through the Baum-Welch algorithm. We present recognition results for discrete and continuous HMMs, for read and spontaneous speech databases, acquired at 8 and 16 kHz. We also present results for a combination of Viterbi and Baum-Welch training, intended as a trade-off...

This paper describes how we can use the generalized BaumWelch (GBW) algorithm to develop better extended BaumWelch (EBW) algorithms. Based on GBW, we show that the backoff term in the EBW algorithm comes from KL-divergence which is used as a regularization function. This finding allows us to develop a fast EBW algorithm, which can reduce the time of model space discriminative training by half, ...

Regime switching volatility models provide a tractable method of modelling stochastic volatility. Currently the most popular method of regime switching calibration is the Hamilton filter. We propose using the Baum-Welch algorithm, an established technique from Engineering, to calibrate regime switching models instead. We demonstrate the Baum-Welch algorithm and discuss the significant advantage...

Regime switching volatility models provide a tractable method of modelling stochastic volatility. Currently the most popular method of regime switching calibration is the Hamilton filter. We propose using the Baum-Welch algorithm, an established technique from Engineering, to calibrate regime switching models instead. We demonstrate the Baum-Welch algorithm and discuss the significant advantage...

In this paper, the theory of hidden Markov models (HMM) is applied to the problem of blind (without training sequences) channel estimation and data detection. Within a HMM framework, the Baum–Welch (BW) identification algorithm is frequently used to find out maximum-likelihood (ML) estimates of the corresponding model. However, such a procedure assumes the model (i.e., the channel response) to ...