Dynamic bayesian networks representation inference and learning phd thesis

Dynamic bayesian networks representation inference and learning phd thesis

In particular, the main novel technical contributions of this thesis are as follows: a way of representing Hierarchical HMMs as DBNs, which enables inference to be done in O(T) time instead of O(T 3), where T is the length of the sequence; an exact smoothing algorithm that takes O(log T) space instead of O(T); a simple way of using the junction tree algorithm for online inference in DBNs; new complexity bounds on exact online inference in DBNs; a new deterministic approximate inference algorithms for learning parameters and structure of CTBN models from both fully ob-served and partially observed data. We prove that the structure learning problem for CTBNs is easier than for traditional BNs or dynamic Bayesian networks (DBNs). We develop an inference algorithm for CTBNs which is a variant of expectation propaga- Apr 19,  · Star 1. Code Issues Pull requests. The software includes a dynamic bayesian network with genetic feature space selection, includes 5 econometric blogger.com with time series. machine-learning r statistics time-series modeling genetic-algorithm financial series econometrics forecasting computational bayesian-networks dbn dynamic-bayesian

Dynamic Bayesian network - Wikipedia

Modelling sequential data is important in many areas of science and engineering. Hidden Markov models HMMs and Kalman filter models KFMs are popular for this because they are simple and flexible. For example, HMMs have been used for speech recognition and bio-sequence analysis, and KFMs have been used for problems ranging from tracking planes and missiles to predicting the economy. Dynamic Bayesian Networks DBNs generalize HMMs by allowing the state space to be represented in factored form, instead of as a single discrete random variable.

DBNs generalize KFMs by allowing arbitrary probability distributions, not just unimodal linear-Gaussian. In this thesis, I will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in DBNs, and how to learn DBN models from sequential data.

In particular, the main novel technical contributions of this thesis are as follows: a way of representing Hierarchical HMMs as DBNs, which enables inference to be done in O T time instead of O T 3where T is the length of the sequence; an exact smoothing algorithm that takes O log T space instead of O T ; a dynamic bayesian networks representation inference and learning phd thesis way of using the junction tree algorithm for online inference in DBNs; new complexity bounds on exact online inference in DBNs; a new deterministic approximate inference algorithm called factored frontier; an analysis of the relationship between the BK algorithm and loopy belief propagation; a way of applying Rao-Blackwellised particle filtering to DBNs in general, and the SLAM simultaneous localization and mapping problem in particular; a way of extending the structural EM algorithm to DBNs; and a variety of different applications of DBNs.

However, perhaps the main value of the thesis is its catholic presentation of the field of sequential data modelling. dynamic bayesian network sequential data new deterministic approximate inference algorithm dbn model catholic presentation expressive power online inference structural em algorithm exact smoothing algorithm speech recognition sequential data modelling rao-blackwellised particle main value kalman filter model loopy belief propagation junction tree algorithm dbns generalize kfms simultaneous localization bio-sequence analysis arbitrary probability distribution bk algorithm exact online inference many different kind simple way new complexity bound main novel technical contribution state space different application many area single discrete random factored form hidden markov model hierarchical hmms approximate inference, dynamic bayesian networks representation inference and learning phd thesis.

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Citations: - 3 self. Summary Citations Active Bibliography Co-citation Clustered Documents Version History. Abstract Modelling sequential data is important in many areas of science and engineering. Keyphrases dynamic bayesian network sequential data new deterministic approximate inference algorithm dbn model catholic presentation expressive power online inference structural em algorithm exact smoothing algorithm speech recognition sequential data modelling rao-blackwellised particle main value kalman filter model loopy belief propagation junction tree algorithm dbns generalize kfms simultaneous localization bio-sequence analysis arbitrary probability distribution bk algorithm exact online inference many different kind simple way new complexity bound main novel technical contribution state space different application many area single discrete random factored form hidden markov model hierarchical hmms approximate inference.

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Dynamic Bayesian Networks

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CiteSeerX — Dynamic Bayesian Networks: Representation, Inference and Learning

Dynamic Bayesian Networks: Representation, Inference and Learning by Kevin Patrick Murphy B.A. Hon. (Cambridge University) M.S. (University of Pennsylvania) A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, BERKELEY In this thesis I address the important problem of the determination of the structure of directed statistical models, with the widely used class of Bayesian network models as a concrete vehicle of my ideas. The structure of a Bayesian network represents a set of conditional If all arcs are directed, both within and between slices, the model is called a dynamic Bayesian network (DBN). (The term “dynamic” means we are modelling a dynamic system, and does not mean the our discussion of offline inference in temporal models is also applicable to atemporal models. The online inference 2 Representation In an File Size: KB