Symbolic Probabilistic Inference in large BN20 networks

D'Ambrosio, Bruce

doi:10.1016/b978-1-55860-332-5.50022-5

Cited by 10 publications

(8 citation statements)

References 4 publications

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“…The positive ndings, on the other hand, are more problematic. In the worst case the exact calculation of posterior probabilities is exponentially costly in the number of positive ndings (Heckerman, 1989;D'Ambrosio, 1994). Moreover, in practical diagnostic situations the number of positive ndings often exceeds the feasible limit for exact calculations.…”

Section: Inferencementioning

confidence: 99%

Variational Probabilistic Inference and the QMR-DT Network

Jaakkola¹,

Jordan²

1999

jair

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We describe a variational approximation method for e cient inference in large-scale probabilistic models. Variational methods are deterministic procedures that provide approximations to marginal and conditional probabilities of interest. They provide alternatives to approximate inference methods based on stochastic sampling or search. We describe a v ariational approach to the problem of diagnostic inference in the Quick Medical Reference" QMR network. The QMR network is a large-scale probabilistic graphical model built on statistical and expert knowledge. Exact probabilistic inference is infeasible in this model for all but a small set of cases. We e v aluate our variational inference algorithm on a large set of diagnostic test cases, comparing the algorithm to a state-of-the-art stochastic sampling method.

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Section: Inferencementioning

confidence: 99%

Variational Probabilistic Inference and the QMR-DT Network

Jaakkola¹,

Jordan²

1999

jair

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“…D'Ambrosio [11,12], Shachter, D'Ambrosio, and del Favero [52], and Li and D'Ambrosio ( [36] 1994) developed an algebraic view to the inference problem. Consider that the full joint probability of a problem domain defined by n variables is the product of the conditional probability of each variable given its parents, and it is uniquely defined given the network.…”

Section: Symbolic Manipulation Algebramentioning

confidence: 99%

Inference Algorithms in Bayesian Networks and the Probanet System

Pan¹,

McMichael²,

Lendjel³

1998

Digital Signal Processing

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“…Pearl also showed how to exploit this structure to speed up probabilistic inference in singly connected networks. Later, this was extended for bipartite graphs with a noisy‐OR interaction . The noisy‐OR gate was then generalized in several ways and was also used to speed up inference in general Bayesian networks …”

Section: Related Workmentioning

confidence: 99%

Modeling the Interactions between Discrete and Continuous Causal Factors in Bayesian Networks

Lucas

Hommersom

2015

Int. J. Intell. Syst.

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The theory of causal independence is frequently used to facilitate the assessment of the probabilistic parameters of discrete probability distributions of complex Bayesian networks. Although it is possible to include continuous parameters in Bayesian networks as well, such parameters could not, so far, be modelled by means of causal independence theory, as a theory of continuous causal independence was not available. In this paper, such a theory is developed and generalised such that it allows merging continuous with discrete parameters based on the characteristics of the problem at hand. This new theory is based on the discovered relationship between the theory of causal independence and convolution in probability theory, discussed for the first time in this paper. It is also illustrated how this new theory can be used in connection with special probability distributions.

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Symbolic Probabilistic Inference in large BN20 networks

Cited by 10 publications

References 4 publications

Variational Probabilistic Inference and the QMR-DT Network

Variational Probabilistic Inference and the QMR-DT Network

Inference Algorithms in Bayesian Networks and the Probanet System

Modeling the Interactions between Discrete and Continuous Causal Factors in Bayesian Networks

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