14,181 research outputs found
From Influence Diagrams to Junction Trees
We present an approach to the solution of decision problems formulated as
influence diagrams. This approach involves a special triangulation of the
underlying graph, the construction of a junction tree with special properties,
and a message passing algorithm operating on the junction tree for computation
of expected utilities and optimal decision policies.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
Efficient Value of Information Computation
One of the most useful sensitivity analysis techniques of decision analysis
is the computation of value of information (or clairvoyance), the difference in
value obtained by changing the decisions by which some of the uncertainties are
observed. In this paper, some simple but powerful extensions to previous
algorithms are introduced which allow an efficient value of information
calculation on the rooted cluster tree (or strong junction tree) used to solve
the original decision problem.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999
Lazy Evaluation of Symmetric Bayesian Decision Problems
Solving symmetric Bayesian decision problems is a computationally intensive
task to perform regardless of the algorithm used. In this paper we propose a
method for improving the efficiency of algorithms for solving Bayesian decision
problems. The method is based on the principle of lazy evaluation - a principle
recently shown to improve the efficiency of inference in Bayesian networks. The
basic idea is to maintain decompositions of potentials and to postpone
computations for as long as possible. The efficiency improvements obtained with
the lazy evaluation based method is emphasized through examples. Finally, the
lazy evaluation based method is compared with the hugin and valuation-based
systems architectures for solving symmetric Bayesian decision problems.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999
Solving Influence Diagrams using HUGIN, Shafer-Shenoy and Lazy Propagation
In this paper we compare three different architectures for the evaluation of
influence diagrams: HUGIN, Shafer-Shenoy, and Lazy Evaluation architecture. The
computational complexity of the architectures are compared on the LImited
Memory Influence Diagram (LIMID): a diagram where only the requiste information
for the computation of the optimal policies are depicted. Because the requsite
information is explicitly represented in the LIMID the evaluation can take
advantage of it, and significant savings in computational can be obtained. In
this paper we show how the obtained savings is considerably increased when the
computations performed on the LIMID is according to the Lazy Evaluation scheme.Comment: Appears in Proceedings of the Seventeenth Conference on Uncertainty
in Artificial Intelligence (UAI2001
From influence diagrams to multi-operator cluster DAGs
There exist several architectures to solve influence diagrams using local
computations, such as the Shenoy-Shafer, the HUGIN, or the Lazy Propagation
architectures. They all extend usual variable elimination algorithms thanks to
the use of so-called 'potentials'. In this paper, we introduce a new
architecture, called the Multi-operator Cluster DAG architecture, which can
produce decompositions with an improved constrained induced-width, and
therefore induce potentially exponential gains. Its principle is to benefit
from the composite nature of influence diagrams, instead of using uniform
potentials, in order to better analyze the problem structure.Comment: Appears in Proceedings of the Twenty-Second Conference on Uncertainty
in Artificial Intelligence (UAI2006
Evaluating Influence Diagrams using LIMIDs
We present a new approach to the solution of decision problems formulated as
influence diagrams. The approach converts the influence diagram into a simpler
structure, the LImited Memory Influence Diagram (LIMID), where only the
requisite information for the computation of optimal policies is depicted.
Because the requisite information is explicitly represented in the diagram, the
evaluation procedure can take advantage of it. In this paper we show how to
convert an influence diagram to a LIMID and describe the procedure for finding
an optimal strategy. Our approach can yield significant savings of memory and
computational time when compared to traditional methods.Comment: Appears in Proceedings of the Sixteenth Conference on Uncertainty in
Artificial Intelligence (UAI2000
Evaluating influence diagrams with decision circuits
Although a number of related algorithms have been developed to evaluate
influence diagrams, exploiting the conditional independence in the diagram, the
exact solution has remained intractable for many important problems. In this
paper we introduce decision circuits as a means to exploit the local structure
usually found in decision problems and to improve the performance of influence
diagram analysis. This work builds on the probabilistic inference algorithms
using arithmetic circuits to represent Bayesian belief networks [Darwiche,
2003]. Once compiled, these arithmetic circuits efficiently evaluate
probabilistic queries on the belief network, and methods have been developed to
exploit both the global and local structure of the network. We show that
decision circuits can be constructed in a similar fashion and promise similar
benefits.Comment: Appears in Proceedings of the Twenty-Third Conference on Uncertainty
in Artificial Intelligence (UAI2007
Propagation of 2-Monotone Lower Probabilities on an Undirected Graph
Lower and upper probabilities, also known as Choquet capacities, are widely
used as a convenient representation for sets of probability distributions. This
paper presents a graphical decomposition and exact propagation algorithm for
computing marginal posteriors of 2-monotone lower probabilities (equivalently,
2-alternating upper probabilities).Comment: Appears in Proceedings of the Twelfth Conference on Uncertainty in
Artificial Intelligence (UAI1996
An Empirical Evaluation of Possible Variations of Lazy Propagation
As real-world Bayesian networks continue to grow larger and more complex, it
is important to investigate the possibilities for improving the performance of
existing algorithms of probabilistic inference. Motivated by examples, we
investigate the dependency of the performance of Lazy propagation on the
message computation algorithm. We show how Symbolic Probabilistic Inference
(SPI) and Arc-Reversal (AR) can be used for computation of clique to clique
messages in the addition to the traditional use of Variable Elimination (VE).
In addition, the paper resents the results of an empirical evaluation of the
performance of Lazy propagation using VE, SPI, and AR as the message
computation algorithm. The results of the empirical evaluation show that for
most networks, the performance of inference did not depend on the choice of
message computation algorithm, but for some randomly generated networks the
choice had an impact on both space and time performance. In the cases where the
choice had an impact, AR produced the best results.Comment: Appears in Proceedings of the Twentieth Conference on Uncertainty in
Artificial Intelligence (UAI2004
Approximate Learning in Complex Dynamic Bayesian Networks
In this paper we extend the work of Smith and Papamichail (1999) and present
fast approximate Bayesian algorithms for learning in complex scenarios where at
any time frame, the relationships between explanatory state space variables can
be described by a Bayesian network that evolve dynamically over time and the
observations taken are not necessarily Gaussian. It uses recent developments in
approximate Bayesian forecasting methods in combination with more familiar
Gaussian propagation algorithms on junction trees. The procedure for learning
state parameters from data is given explicitly for common sampling
distributions and the methodology is illustrated through a real application.
The efficiency of the dynamic approximation is explored by using the Hellinger
divergence measure and theoretical bounds for the efficacy of such a procedure
are discussed.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999
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