Using the reference probability method, a recursive equation is obtained for the unnormalized joint conditional density of a noisily observed Markov chain, and parameters which determine the transition densities and coefficients in the observations.

An original procedure for estimating the model of a noncausal Gauss-Markov Random Field (GMRF) bservations is proposed. Starting from a suitable ’local’ on of the field and taking into account the symmetry the so-called ’potential fields’ [3] describing the GMRF, uation system relating the model parameters to the on-stationary) 2D autocorrelation function (acf) of the ld is derived. Its solutio...

As an effort to make prosody useful in spontaneous speech recognition, we adopt a quasi-continuous prosodic annotation and accordingly design a prosody-dependent acoustic model to improve ASR performances. We propose a variable-parameter Hidden Markov Models, modeling the mean vector as a function of the prosody variable through a polynomial regression model. The prosodically-adapted acoustic m...

Rational Arnoldi is a powerful method for approximating functions of large sparse matrices times a vector. The selection of asymptotically optimal parameters for this method is crucial for its fast convergence. We present a heuristic for the automated pole selection when the function to be approximated is of Markov type, such as the matrix square root. The performance of this approach is demons...

investors use different approaches to select optimal portfolio. so, optimal investment choices according to return can be interpreted in different models. the traditional approach to allocate portfolio selection called a mean - variance explains. another approach is markov chain. markov chain is a random process without memory. this means that the conditional probability distribution of the nex...

Models for spatial point processes are characterized as the stationary distributions of spatial birth and death processes. The Markov chain Monte Carlo algorithm determined by the underlying birth and death process immediately gives a method of simulation, and the time-invariance method of estimation proposed by Baddeley (2000) gives a general method for deriving parameter estimates for the mod...

This paper considers on-line identification of hidden Markov models via multiple-prediction-horizon recursive prediction error (RPE) methods. Working with multiple prediction horizons ensures that there is consistent parameter estimation, under appropriate excitation conditions. Simulation studies are included to illustrate the advantages of the proposed approach when compared to standard metho...

In this demo, we present a technique for synthesizing the mouth movement from acoustic speech information. The algorithm maps the audio parameter set to the visual parameter set using the Gaussian Mixture Model and the Hidden Markov Model. With this technique, we can create smooth and realistic lip movements.

Hidden Markov models (HMM) are successfully applied in various elds of time series analysis. Colored noise, e.g. due to ltering, violates basic assumptions of the model. While it is well-known how to consider auto-regressive (AR) ltering, there is no algorithm to take into account moving-average (MA) ltering in parameter estimation exactly. We present an approximate likelihood estimator for MA-...

This paper defines a new sequence of finite dimensional algebras as quotients of the group algebras of the braid groups. This sequence depends on three homogeneous parameters and has a one-parameter family of Markov traces, and so gives a three parameter invariant of oriented links.