We derive a Markov chain to sample from the posterior distribution for a phylogenetic tree given sequence information from the corresponding set of organisms, a stochastic model for these data, and a prior distribution on the space of trees. A transformation of the tree into a canonical cophenetic matrix form suggests a simple and effective proposal distribution for selecting candidate trees cl...

We study the Abelian sandpile model on Z. In d ≥ 3 we prove existence of the infinite volume addition operator, almost surely with respect to the infinite volume limit μ of the uniform measures on recurrent configurations. We prove the existence of a Markov process with stationary measure μ, and study ergodic properties of this process. The main techniques we use are a connection between the st...

the notion of infinite order markov process is introduced and the markov property of the flow of information is established.

In this short paper we consider the equivalence between various load-scheduling policies and continuous time Markov chains. It provides a basic unification of both divisible load scheduling and Markov chains for the first time in 15 years of research. This unification is suggested by the fact that both divisible load scheduling theory and Markovian queueing theory have common features. I. Backg...

In this paper, we propose a new feature extraction method that combines both HMT-based denoising and weighted filter bank analysis for robust speech recognition. The proposed method is made up of two stages in cascade. The first stage is denoising process based on the wavelet domain Hidden Markov Tree model, and the second one is the filter bank analysis with weighting coefficients obtained fro...

This paper describes a novel approach for incremental learning of motion pattern primitives through long-term observation of human motion. Human motion patterns are abstracted into a factorial hidden Markov model representation, which can be used for both subsequent motion recognition and generation. The model size is adaptable based on the discrimination requirements in the associated region o...

Reinforcement learning (RL) in partially observable settings is challenging because the agent’s observations are not Markov. Recently proposed methods can learn variable-order Markov models of the underlying process but have steep memory requirements and are sensitive to aliasing between observation histories due to sensor noise. This paper proposes dynamic-depth context tree weighting (D2-CTW)...

When analyzing the reliability of a time-constrained distributed software, the Markov state space is often used. However, many evaluation methods are not fast and the size of Markov state space is very large. In this paper, we propose an approach for the reliability analysis of distributed software with time constraint. The approach proposed is based on the principle of distributed program reli...

This thesis describes English-Czech Machine Translation as it is implemented in TectoMT system. The transfer uses deep-syntactic dependency (tectogrammatical) trees and exploits the annotation scheme of Prague Dependency Treebank. The primary goal of the thesis is to improve the translation quality using both rule-base and statistical methods. First, we present a manual annotation of translatio...

The tree-width of a graph is of great importance in applied problems in graphical models. The complexity of inference problems on Markov random fields is exponential in the treewidth of the graph. However, computing tree-width is NP-hard in general. Easily computable upper bounds exist, but there are few lower bounds. We give a novel technique to compute a lower bound for the tree-width of a gr...