Numerous recent studies suggest that human learners, including both infants and adults, readily track sequential statistics computed between adjacent elements. One such statistic, transitional probability, is typically calculated as the likelihood that one element predicts another. However, little is known about whether listeners are sensitive to the directionality of this computation. To addre...

In this paper, we propose an improvement of hidden semiMarkov model (HSMM) based speech synthesis system by durationdependent state transition probabilities. In traditional HMM algorithm, the probability of the duration of a state decreases exponentially with time, which does not provide an adequate representation of the temporal structure of speech. To overcome this limitation, HSMM, which mod...

and artificial intelligence may be viewed as instances of the summary-product algorithm (or belief/probability propagation algorithm), which operates by message passing in a graphical model. Specific instances of such algorithms include Kalman filtering and smoothing; the forward–backward algorithm for hidden Markov models; probability propagation in Bayesian networks; and decoding algorithms f...

This article presents a hidden Markov model designed to screen a telephone, computer , or other type of account for criminal activity. An augmented variables sampler is used to draw model parameters from their posterior distribution. Using estimated parameters , the forward-backward algorithm for hidden Markov models rapidly computes the probability of a criminal presence on the account at any ...

Segmental structure is a common pattern in many types of sequences such as phrases in human languages. In this paper, we present a probabilistic model for sequences via their segmentations. The probability of a segmented sequence is calculated as the product of the probabilities of all its segments, where each segment is modeled using existing tools such as recurrent neural networks. Since the ...

In this paper, we revisit the forward, backward and bidirectional Bahl-Cocke-Jelinek-Raviv (BCJR) soft-input soft-output (SISO) maximum a posteriori probability (MAP) decoding process of rate-1 convolutional codes. From this we establish some interesting duality properties between encoding and decoding of rate-1 convolutional codes. We observe that the forward and backward BCJR SISO MAP decoder...

The subject of probability theory is widely established in colleges and universities. Probability a very important subject, whether it the usual final examination or many students' postgraduate entrance examination. This has certain requirements for teaching theory, but there are problems universities, such as backward methods low students, which have greatly affected learning, require college ...

probability of private information based trade (pin) has introduced as information risk measure. this paper is going to estimate probability of private information based trade (pin) in tehran stock exchange using microstructure models. our results show that pin is significantly different from zero for tehran stock exchange.

since the rate of return on different opportunities for investment is random, the optimal distribution of ones capital is put foreward as a stochastic control problem. in this paper, using modem techniques in the theory of random functions and the stochsatic calculus, the problem is discussed for the case when the information possessed by the different investors, as well as their probability be...

In this paper, we revisit the forward, backward and bidirectional Bahl-Cocke-Jelinek-Raviv (BCJR) soft-input soft-output (SISO) maximum a posteriori probability (MAP) decoding process of rate-1 binary convolutional codes. From this we establish some interesting explicit relationships between encoding and decoding of rate-1 convolutional codes. We observe that the forward and backward BCJR SISO ...