DeepAI AI Chat
Log In Sign Up

A Channel-based Exact Inference Algorithm for Bayesian Networks

04/21/2018
by   Bart Jacobs, et al.
Radboud Universiteit
0

This paper describes a new algorithm for exact Bayesian inference that is based on a recently proposed compositional semantics of Bayesian networks in terms of channels. The paper concentrates on the ideas behind this algorithm, involving a linearisation (`stretching') of the Bayesian network, followed by a combination of forward state transformation and backward predicate transformation, while evidence is accumulated along the way. The performance of a prototype implementation of the algorithm in Python is briefly compared to a standard implementation (pgmpy): first results show competitive performance.

READ FULL TEXT

page 14

page 15

10/12/2011

Cutset Sampling for Bayesian Networks

The paper presents a new sampling methodology for Bayesian networks that...
01/23/2013

Inference in Multiply Sectioned Bayesian Networks with Extended Shafer-Shenoy and Lazy Propagation

As Bayesian networks are applied to larger and more complex problem doma...
06/24/2018

Probabilistic Inference Using Generators - The Statues Algorithm

We present here a new probabilistic inference algorithm that gives exact...
04/03/2018

The Logical Essentials of Bayesian Reasoning

This chapter offers an accessible introduction to the channel-based appr...
08/29/2022

Approach of variable clustering and compression for learning large Bayesian networks

This paper describes a new approach for learning structures of large Bay...
12/08/2022

Fast Parallel Exact Inference on Bayesian Networks: Poster

Bayesian networks (BNs) are attractive, because they are graphical and i...
03/27/2013

Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks

Stochastic simulation approaches perform probabilistic inference in Baye...