Inference in Belief Network using Logic Sampling and Likelihood Weighing algorithms

Stochastic Logic Programs: Sampling, Inference and Applications

Algorithms for exact and approximate inference in stochastic logic programs (SLPs) are pre­ sented, based respectively, on variable elimina­ tion and importance sampling. We then show how SLPs can be used to represent prior distri­ butions for machine learning, using (i) logic pro­ grams and (ii) Bayes net structures as examples. Drawing on existing work in statistics, we apply the Metropolis-H...

Analysis of Biological Network Data Using Likelihood-free Inference Techniques

Biological Networks have received much attention in recent years, but statistical tools for network analysis are still in their infancy. In this paper we focus on Protein Interaction Networks (PINs) that typically comprise thousands of proteins and interactions. PINs are the result of long evolutionary histories. Here we adopt simple mathematical models that capture essentials of protein evolut...

In All Likelihood - Statistical Modeling and Inference Using Likelihood

Genetic Algorithms for Belief Network

Belief networks are graphical models that encode probability distributions; they are used for reasoning and learning under uncertainty. Genetic algorithms &re inspired by nature, and are used for adaptation, search, optimization, and learning in complex environments. This paper focuses on the use of a belief network as a genetic algorithm fitness function, and presents theoretical and empirical...

Genetic algorithms and parallel processing in maximum-likelihood phylogeny inference.

We investigated the usefulness of a parallel genetic algorithm for phylogenetic inference under the maximum-likelihood (ML) optimality criterion. Parallelization was accomplished by assigning each "individual" in the genetic algorithm "population" to a separate processor so that the number of processors used was equal to the size of the evolving population (plus one additional processor for the...