16,450 research outputs found
An Empirical Evaluation of a Randomized Algorithm for Probabilistic Inference
In recent years, researchers in decision analysis and artificial intelligence
(Al) have used Bayesian belief networks to build models of expert opinion.
Using standard methods drawn from the theory of computational complexity,
workers in the field have shown that the problem of probabilistic inference in
belief networks is difficult and almost certainly intractable. K N ET, a
software environment for constructing knowledge-based systems within the
axiomatic framework of decision theory, contains a randomized approximation
scheme for probabilistic inference. The algorithm can, in many circumstances,
perform efficient approximate inference in large and richly interconnected
models of medical diagnosis. Unlike previously described stochastic algorithms
for probabilistic inference, the randomized approximation scheme computes a
priori bounds on running time by analyzing the structure and contents of the
belief network. In this article, we describe a randomized algorithm for
probabilistic inference and analyze its performance mathematically. Then, we
devote the major portion of the paper to a discussion of the algorithm's
empirical behavior. The results indicate that the generation of good trials
(that is, trials whose distribution closely matches the true distribution),
rather than the computation of numerous mediocre trials, dominates the
performance of stochastic simulation. Key words: probabilistic inference,
belief networks, stochastic simulation, computational complexity theory,
randomized algorithms.Comment: Appears in Proceedings of the Fifth Conference on Uncertainty in
Artificial Intelligence (UAI1989
A Stratified Simulation Scheme for Inference in Bayesian Belief Networks
Simulation schemes for probabilistic inference in Bayesian belief networks
offer many advantages over exact algorithms; for example, these schemes have a
linear and thus predictable runtime while exact algorithms have exponential
runtime. Experiments have shown that likelihood weighting is one of the most
promising simulation schemes. In this paper, we present a new simulation scheme
that generates samples more evenly spread in the sample space than the
likelihood weighting scheme. We show both theoretically and experimentally that
the stratified scheme outperforms likelihood weighting in average runtime and
error in estimates of beliefs.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
Backward Simulation in Bayesian Networks
Backward simulation is an approximate inference technique for Bayesian belief
networks. It differs from existing simulation methods in that it starts
simulation from the known evidence and works backward (i.e., contrary to the
direction of the arcs). The technique's focus on the evidence leads to improved
convergence in situations where the posterior beliefs are dominated by the
evidence rather than by the prior probabilities. Since this class of situations
is large, the technique may make practical the application of approximate
inference in Bayesian belief networks to many real-world problems.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
Forecasting Sleep Apnea with Dynamic Network Models
Dynamic network models (DNMs) are belief networks for temporal reasoning. The
DNM methodology combines techniques from time series analysis and probabilistic
reasoning to provide (1) a knowledge representation that integrates
noncontemporaneous and contemporaneous dependencies and (2) methods for
iteratively refining these dependencies in response to the effects of exogenous
influences. We use belief-network inference algorithms to perform forecasting,
control, and discrete event simulation on DNMs. The belief network formulation
allows us to move beyond the traditional assumptions of linearity in the
relationships among time-dependent variables and of normality in their
probability distributions. We demonstrate the DNM methodology on an important
forecasting problem in medicine. We conclude with a discussion of how the
methodology addresses several limitations found in traditional time series
analyses.Comment: Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993
Importance Sampling via Variational Optimization
Computing the exact likelihood of data in large Bayesian networks consisting
of thousands of vertices is often a difficult task. When these models contain
many deterministic conditional probability tables and when the observed values
are extremely unlikely even alternative algorithms such as variational methods
and stochastic sampling often perform poorly. We present a new importance
sampling algorithm for Bayesian networks which is based on variational
techniques. We use the updates of the importance function to predict whether
the stochastic sampling converged above or below the true likelihood, and
change the proposal distribution accordingly. The validity of the method and
its contribution to convergence is demonstrated on hard networks of large
genetic linkage analysis tasks.Comment: Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007
IDEAL: A Software Package for Analysis of Influence Diagrams
IDEAL (Influence Diagram Evaluation and Analysis in Lisp) is a software
environment for creation and evaluation of belief networks and influence
diagrams. IDEAL is primarily a research tool and provides an implementation of
many of the latest developments in belief network and influence diagram
evaluation in a unified framework. This paper describes IDEAL and some lessons
learned during its development.Comment: Appears in Proceedings of the Sixth Conference on Uncertainty in
Artificial Intelligence (UAI1990
Loopy Belief Propagation for Approximate Inference: An Empirical Study
Recently, researchers have demonstrated that loopy belief propagation - the
use of Pearls polytree algorithm IN a Bayesian network WITH loops OF error-
correcting codes.The most dramatic instance OF this IS the near Shannon - limit
performance OF Turbo Codes codes whose decoding algorithm IS equivalent TO
loopy belief propagation IN a chain - structured Bayesian network. IN this
paper we ask : IS there something special about the error - correcting code
context, OR does loopy propagation WORK AS an approximate inference schemeIN a
more general setting? We compare the marginals computed using loopy propagation
TO the exact ones IN four Bayesian network architectures, including two real -
world networks : ALARM AND QMR.We find that the loopy beliefs often converge
AND WHEN they do, they give a good approximation TO the correct
marginals.However,ON the QMR network, the loopy beliefs oscillated AND had no
obvious relationship TO the correct posteriors. We present SOME initial
investigations INTO the cause OF these oscillations, AND show that SOME simple
methods OF preventing them lead TO the wrong results.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999
Error Estimation in Approximate Bayesian Belief Network Inference
We can perform inference in Bayesian belief networks by enumerating
instantiations with high probability thus approximating the marginals. In this
paper, we present a method for determining the fraction of instantiations that
has to be considered such that the absolute error in the marginals does not
exceed a predefined value. The method is based on extreme value theory.
Essentially, the proposed method uses the reversed generalized Pareto
distribution to model probabilities of instantiations below a given threshold.
Based on this distribution, an estimate of the maximal absolute error if
instantiations with probability smaller than u are disregarded can be made.Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995
Incremental Dynamic Construction of Layered Polytree Networks
Certain classes of problems, including perceptual data understanding,
robotics, discovery, and learning, can be represented as incremental,
dynamically constructed belief networks. These automatically constructed
networks can be dynamically extended and modified as evidence of new
individuals becomes available. The main result of this paper is the incremental
extension of the singly connected polytree network in such a way that the
network retains its singly connected polytree structure after the changes. The
algorithm is deterministic and is guaranteed to have a complexity of single
node addition that is at most of order proportional to the number of nodes (or
size) of the network. Additional speed-up can be achieved by maintaining the
path information. Despite its incremental and dynamic nature, the algorithm can
also be used for probabilistic inference in belief networks in a fashion
similar to other exact inference algorithms.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
Fast Belief Update Using Order-of-Magnitude Probabilities
We present an algorithm, called Predict, for updating beliefs in causal
networks quantified with order-of-magnitude probabilities. The algorithm takes
advantage of both the structure and the quantification of the network and
presents a polynomial asymptotic complexity. Predict exhibits a conservative
behavior in that it is always sound but not always complete. We provide
sufficient conditions for completeness and present algorithms for testing these
conditions and for computing a complete set of plausible values. We propose
Predict as an efficient method to estimate probabilistic values and illustrate
its use in conjunction with two known algorithms for probabilistic inference.
Finally, we describe an application of Predict to plan evaluation, present
experimental results, and discuss issues regarding its use with conditional
logics of belief, and in the characterization of irrelevance.Comment: Appears in Proceedings of the Eleventh Conference on Uncertainty in
Artificial Intelligence (UAI1995
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