2 edition of **Representations and algorithms for efficient inference in Bayesian networks** found in the catalog.

Representations and algorithms for efficient inference in Bayesian networks

Masami Takikawa

- 104 Want to read
- 7 Currently reading

Published
**1998**
.

Written in English

- Neural networks (Computer science),
- Machine learning.,
- Bayesian statistical decision theory.

**Edition Notes**

Statement | by Masami Takikawa. |

The Physical Object | |
---|---|

Pagination | 85 leaves, bound : |

Number of Pages | 85 |

ID Numbers | |

Open Library | OL15529907M |

Abstract: A belief network comprises a graphical representation of dependencies between variables of a domain and a set of conditional probabilities associated with each dependency. Unless rho =NP, an efficient, exact algorithm does not exist to compute probabilistic inference in belief networks. Stochastic simulation methods, which often improve run times, provide an alternative to exact. Here, we present a novel protein inference method, EPIFANY, combining a loopy belief propagation algorithm with convolution trees for efficient processing of Bayesian networks. We demonstrate that EPIFANY combines the reliable protein inference of Bayesian methods with .

Bayesian networks are a very general and powerful tool that can be used for a large number of problems involving uncertainty: reasoning, learning, planning and perception. They provide a language that supports efficient algorithms for the automatic construction of expert systems in several different contexts. The range of applications of Bayesian networks currently extends over almost all. Inference in Bayesian Networks In this chapter we will cover the major classes of inference algorithms — exact and approximate — that have been developed over the past 20 years. As we will see, different algorithms are suited to different network structures and performance requirements.

Another inference algorithm is developed for type 1 similarity networks that works under no restriction, albeit less efficiently. Do you want to read the rest of this article? Request full-text. A very useful graph is provided to help readers understand the dependencies between the chapters. The author proposes some ways that his book could be used in different lectures. This alone is proof that the author has strong experience in teaching information theory, inference, and learning algorithms.

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Section 2 gives an overview of Bayesian network inference algorithms in the literature. Section 3 gives a brief introduction of the unscented Kalman filter, which is used in the proposed method. Section 4 develops the proposed method and Section 5 provides two numerical examples.

Overview of inference algorithms for static/dynamic BN Cited by: 6. A powerful set of computational routines complements the representational utility of Bayesian networks, and the second part of this paper describes these algorithms in detail. We present a novel view of inference in general networks – where inference is done via a change-of-variables that renders the network tree-structured and amenable to a Cited by: Abstract: Latent Dirichlet allocation (LDA) is a Bayesian network that has recently gained much popularity in applications ranging from document modeling to computer vision.

Due to the large scale nature of these applications, current inference procedures like variational Bayes and Gibbs sampling have been found lacking. For mobile robots to operate in real environments, it is essential that basic tasks such as localization, mapping and navigation are performed properl.

For Bayesian networks of tree structure, simpler message-passing algorithms exist, e.g., the sum-product algorithm [bishoppattern].

As this paper is the first work to address materialization for Bayesian networks, we opt to work with variable elimination [zhangsimple] due to its conceptual simplicity. However, it is possible that a. Efficient Inference for Mixed Bayesian Networks K.

Chang Dept. of Systems Engineering and Operation Research George Mason University Fairfax, VA [email protected] Zhi Tian Dept. of Electrical and Computer Engineering Michigan Technological University Houghton, MI [email protected] Abstract-Bayesian network is a compact representation. New approach using Bayesian Network to improve content based algorithm grow in complexity and execution time.

Zhao et al. in [28] divide the existing algorithms into three representations and recognition algorithms. Holistic Approach Holistic algorithms use global features of the complete face.

Examples of holistic systems include. of excellent algorithms for learning Bayesian networks from data. However, by there still seemed to be no accessible source for ‘learning Bayesian networks.’ Similar to my purpose a decade ago, the goal of this text is to provide such a source.

In order to make this text a complete introduction to Bayesian networks, I discuss methods. Chapter 1 presents background material on Bayesian inference, graphical models, and propaga-tion algorithms. Chapter 2 forms the theoretical core of the thesis, generalising the expectation-maximisation (EM) algorithm for learning maximum likelihood parameters to the VB EM al-gorithm which integrates over model parameters.

Bayesian Networks Structured, graphical representation of probabilistic relationships between several random variables Explicit representation of conditional independencies Missing arcs encode conditional independence Efficient representation of joint PDF P(X) Generative model (not just discriminative): allows arbitrary queries to be answered.

As Bayesian networks are applied to more complex and realistic real-world applications, the development of more efficient inference algorithms working under real-time constraints is. Chapter 11 is on the complexity of probabilistic inference for belief update, MAP, and MPE.

Chap "Compiling Bayesian Networks," covers mapping a BN to a Boolean function computed offline in a preprocessing phase. The Boolean function is used for efficient inference, online, on multiple queries, using circuit propagation.

Artificial Intelligence ELSEVIER Artificial Intelligence 82 () Knowledge representation and inference in similarity networks and Bayesian multinets Dan Geiger8'*, David Heckerman1' " Department of Computer Science, Technion Israel Institute of Technology, HaifaIsrael h Microsoft Corporation, One Microsoft Way, Redmond, WAUSA Received.

A key problem is distributed inference, mapping representations and techniques such as graphical models or dynamic Bayesian networks to sensor networks.

Information double counting, as discussed earlier, is a major problem for distributed inference and maybe addressed using approximate algorithms [. A number of exact algorithms have been developed to perform probabilistic inference in Bayesian belief networks in recent years.

These algorithms use graph-theoretic techniques to analyze and. Abstract. This chapter introduces Bayesian networks, covering representation and inference. The basic representational aspects of a Bayesian network are presented, including the concept of D-Separation and the independence axioms.

result in the evidence propagation across the Bayesian networks. This paper sums up various inference techniques in Bayesian networks and provide guidance for the algorithm calculation in probabilistic inference in Bayesian networks.

1 Introduction Because a Bayesian network is a complete model for the variables and their. In this paper, we use general purpose processor (GPP) and general purpose graphics processing unit (GPGPU) to implement and accelerate a novel Bayesian network learning algorithm.

With a hash-table-based memory-saving strategy and a novel task assigning strategy, we achieve a fold acceleration per iteration than using a serial GPP. This article describes an algorithm that solves the problem of finding the K most probable configurations of a Bayesian network, given certain evidence, for any K, and for any type of network, including multiply connected networks.

This algorithm is based on the compilation of the initial network into a junction tree. After a description of the preliminary steps needed to get a junction tree.

Bayesian belief networks provide a natural, efficient method for representing probabilistic dependencies among a set of variables. For these reasons, numerous researchers are exploring the use of belief networks as a knowledge representation in artificial intelligence. Algorithms have been developed previously for efficient probabilistic inference using special classes of belief networks.

Novel recursive inference algorithm for discrete dynamic Bayesian networks Article (PDF Available) in Progress in Natural Science 19(9) September with 43 Reads How we measure 'reads'.

In this paper, we suggest an algorithm that transforms a FHHMM into a Bayesian Network in order to be able to perform inference. As a matter of fact, inference in Bayesian Network is a well-known mechanism and this representation formalism provides a well grounded theoretical background that may help us to achieve our goal.Next, they require inference in the Bayesian network, which is by itself already intractable (for high-treewidth networks with little local structure (Chavira and Darwiche ; )).

Finally, these algorithms may get stuck in local optima, which means that, in practice, one must run these algorithms multiple times with different ini.