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Multigrid Methods Combined With Low-Rank Approximation For Tensor-Structured Markov Chains

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The Metropolized Partial Importance Sampling MCMC mixes slowly on minimal reversal rearrangement paths

Krister Swenson

Markov chain Monte Carlo has been the standard technique for inferring the posterior distribution of genome rearrangement scenarios under a Bayesian approach. We present here a negative result on the rate of convergence of the generally used Markov chains. ...

2010

Bayesian variable order Markov models

Christos Dimitrakakis

We present a simple, effective generalisation of variable order Markov models to full online Bayesian estimation. The mechanism used is close to that employed in context tree weighting. The main contribution is the addition of a prior, conditioned on conte ...

JMLR2010

Evaluation of a probabilistic approach to learn and reproduce gestures by imitation

Aude Billard, Sylvain Calinon, Eric Sauser

We present an approach based on Hidden Markov Model (HMM) and Gaussian Mixture Regression (GMR) to learn robust models of human motion through imitation. The proposed approach allows us to extract redundancies across multiple demonstrations and build time- ...

2010

Channel estimation by inference on Gaussian Markov random fields

Thomas Riedl

In this paper, we discuss a novel method for channel estimation. The approach is based on the idea of modeling the complex channel gains by a Markov random field. This graphical model is used to capture the statistical dependencies between consecutive taps ...

2009

Sliding Window Abstraction for Infinite Markov Chains

Maria-Emanuela-Canini Mateescu, Verena Wolf

We present ail on-the-fly abstraction technique for infinite-state continuous-time Markov chains. We consider Markov chains that are specified by a finite set of transition classes. Such models naturally represent biochemical reactions and therefore play a ...

Springer-Verlag New York, Ms Ingrid Cunningham, 175 Fifth Ave, New York, Ny 10010 Usa2009

Stochastic Modeling and Control of Biological Systems: The Lactose Regulation System of Escherichia coli

Mahmut Selman Sakar

In this paper, we present a comprehensive framework for stochastic modeling, model abstraction, and controller design for a biological system. The first half of the paper concerns modeling and model abstraction of the system. Most models in systems biology ...

Institute of Electrical and Electronics Engineers2008

Flow faster: efficient decision algorithms for probabilistic simulations

Friedrich Eisenbrand

Abstraction techniques based on simulation relations have become an important and effective proof technique to avoid the infamous state space explosion problem. In the context of Markov chains, strong and weak simulation relations have been proposed ((B. J ...

2007

Some Basic Functions for Tree Representations of Bayesian Markov Chain Monte Carlo Clustering

Anthony Christopher Davison, Vahid Partovi Nia

The paper contains description of the implementation of C code for tree representation of Markov Chain Monte Carlo(MCMC) clustering. The aim of the code is to produce results which helps in visual representation of the most frequent pattern, its agglomerat ...

2007

Spin Glass Models of Markov Random Fields

Barbara Caputo

This paper presents a novel algorithm for robust object recognition. We propose to model the visual appearance of objects via probability density functions. The algorithm consists of a fully connected Markov random field with energy function derived from r ...

2006

Kernel Methods for Melanoma Recognition

Barbara Caputo, Tatiana Tommasi

Skin cancer is a spreading disease in the western world. Early detection and treatment are crucial for improving the patient survival rate. In this paper we present two algorithms for computer assisted diagnosis of melanomas. The first is the support vecto ...

2006