We introduce a new graphical model for tracking radio-tagged animals and learning their movement patterns. The model provides a principled way to combine radio telemetry data with an arbitrary set of userdefined, spatial features. We describe an efficient stochastic gradient algorithm for fitting model parameters to data and demonstrate its effectiveness via asymptotic analysis and synthetic ex...

When learning a hidden Markov model (HMM), sequential observations can often be complemented by real-valued summary response variables generated from the path of hidden states. Such settings arise in numerous domains, including many applications in biology, like motif discovery and genome annotation. In this paper, we present a flexible framework for jointly modeling both latent sequence featur...

Infinite Hidden Markov Models (iHMM’s) are an attractive, nonparametric generalization of the classical Hidden Markov Model which can automatically infer the number of hidden states in the system. However, due to the infinite-dimensional nature of the transition dynamics, performing inference in the iHMM is difficult. In this paper, we present an infinite-state Particle Gibbs (PG) algorithm to ...

This paper presents an approach to realizing various emotional expressions and speaking styles in synthetic speech using HMM-based speech synthesis. We show two methods for modeling speaking styles and emotions. In the first method, called “style dependent modeling,” each speaking style and emotion is individually modeled. On the other hand, in the second method, called “style mixed modeling,” ...

Assessing whether a model is a good fit to the data is non trivial. The standard practice is to compare a few machine learning techniques to learn a model from data, and pick the one with the highest predictive performance. The winner is considered the best fitting model. But each model may involve different machine learning algorithms that carry their own set of parameters and constraints impo...

We inverse the surface gravity data to recover subsurface 3D density distribution with two strategy. In the first strategy, we assumed wide density model bound for inverting gravity data and In the second strategy, the inversion procedure have been carried out by limited bound density. Wediscretize the earth model into rectangular cells of constant andunidentified density. The number of cells i...

Correctly choosing the number of topics plays an important role in successfully applying topic models to real world applications. Following the latest tensor decomposition framework by Anandkumar et al., we make the first attempt to provide theoretical analysis on the number of topics under Latent Dirichlet Allocation model. With mild conditions, our method provides accessible information on th...

We have investigated the performance of a hidden Markov model QBH retrieval system on a large musical database. The database is synthetic, generated from statistics gleaned from our (smaller) database of musical excerpts from various genres. This paper reports the performance of several variations of our retrieval system against different types of synthetic queries on the large database, where ...

this paper presents dynamic portfolio model based on the merton's optimal investment-consumption model, which combines dynamic synthetic put option using risk-free and risky assets. this paper is extended version of methodological paper published by yuan yao (2012) cite{26}. because of the long history of the development of foreign financial market, with a variety of financial derivatives, the ...