The von Mises model encodes a multivariate circular distribution as an undirected probabilistic graphical model. Presently, the only algorithm for performing inference in the model is Gibbs sampling, which becomes inefficient for large graphs. To address this issue, we introduce an Expectation Propagation based algorithm for performing inference in the von Mises graphical model. Our approach in...

In this paper, we consider the main algorithmic difficulties of constructing a Pareto set associated with configuration an array points. On basis coordinate approach «maximin» author’s algorithm construction is made, taking into account these difficulties. The program written in Python. was graphically tested on specific example: sample minimum total price and high «quality» (Harrinkton desirab...

In this article we show the duality between tensor networks and undirected graphical models with discrete variables. We study tensor networks on hypergraphs, which we call tensor hypernetworks. We show that the tensor hypernetwork on a hypergraph exactly corresponds to the graphical model given by the dual hypergraph. We translate various notions under duality. For example, marginalization in a...

Generative models of protein structure enable researchers to predict the behavior of proteins under different conditions. Continuous graphical models are powerful and efficient tools for modeling static and dynamic distributions, which can be used for learning generative models of molecular dynamics. In this thesis, we develop new and improved continuous graphical models, to be used in modeling...

<p>A multitude of research has been rising for predicting the behavior different real-world problems through machine learning models. An erratic nature occurs due to augmented and inadequacy prerequisite dataset prediction water level over fundamental models that show flat or low-set accuracy. In this paper, a powerful scaling strategy is proposed improvised back-propagation algorithm usi...

In this work we develop a graphical model for describing probability distributions over labeled partitions of an undirected graph which are conditioned on observed data. We show how to efficiently perform exact inference in these models, by exploiting the structure of the graph and adapting the sum-product and max-product algorithms. We demonstrate our approach on the task of segmenting and lab...

There is growing evidence that neural circuits may employ statistical algorithms for inference and learning. Many such algorithms can be derived from independence diagrams (graphical models) showing causal relationships between random variables. A general algorithm for inference in graphical models is belief propagation, where nodes in a graphical model determine values for random variables by ...

We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a parameterized distribution. It extends the traditional least Herbrand model semantics in logic programming to distribution semantics , possible world semantics with a probability distribution which is unconditionally a...