1,747 research outputs found
An Axiomatic Framework for Bayesian and Belief-function Propagation
In this paper, we describe an abstract framework and axioms under which exact
local computation of marginals is possible. The primitive objects of the
framework are variables and valuations. The primitive operators of the
framework are combination and marginalization. These operate on valuations. We
state three axioms for these operators and we derive the possibility of local
computation from the axioms. Next, we describe a propagation scheme for
computing marginals of a valuation when we have a factorization of the
valuation on a hypertree. Finally we show how the problem of computing
marginals of joint probability distributions and joint belief functions fits
the general framework.Comment: Appears in Proceedings of the Fourth Conference on Uncertainty in
Artificial Intelligence (UAI1988
Beliefs in Markov Trees - From Local Computations to Local Valuation
This paper is devoted to expressiveness of hypergraphs for which uncertainty
propagation by local computations via Shenoy/Shafer method applies. It is
demonstrated that for this propagation method for a given joint belief
distribution no valuation of hyperedges of a hypergraph may provide with
simpler hypergraph structure than valuation of hyperedges by conditional
distributions. This has vital implication that methods recovering belief
networks from data have no better alternative for finding the simplest
hypergraph structure for belief propagation. A method for recovery
tree-structured belief networks has been developed and specialized for
Dempster-Shafer belief functionsComment: Preliminary versioin of conference paper: M.A. K{\l}opotek: Beliefs
in Markov Trees - From Local Computations to Local Valuation. [in:] R.
Trappl, Ed.: Cybernetics and Systems Research , Proc. 12th European Meeting
on Cybernetics and System Research, Vienna 5-8 April 1994, World Scientific
Publishers, Vol.1. pp. 351-35
Independence, Conditionality and Structure of Dempster-Shafer Belief Functions
Several approaches of structuring (factorization, decomposition) of
Dempster-Shafer joint belief functions from literature are reviewed with
special emphasis on their capability to capture independence from the point of
view of the claim that belief functions generalize bayes notion of probability.
It is demonstrated that Zhu and Lee's {Zhu:93} logical networks and Smets'
{Smets:93} directed acyclic graphs are unable to capture statistical
dependence/independence of bayesian networks {Pearl:88}. On the other hand,
though Shenoy and Shafer's hypergraphs can explicitly represent bayesian
network factorization of bayesian belief functions, they disclaim any need for
representation of independence of variables in belief functions.
Cano et al. {Cano:93} reject the hypergraph representation of Shenoy and
Shafer just on grounds of missing representation of variable independence, but
in their frameworks some belief functions factorizable in Shenoy/Shafer
framework cannot be factored.
The approach in {Klopotek:93f} on the other hand combines the merits of both
Cano et al. and of Shenoy/Shafer approach in that for Shenoy/Shafer approach no
simpler factorization than that in {Klopotek:93f} approach exists and on the
other hand all independences among variables captured in Cano et al. framework
and many more are captured in {Klopotek:93f} approach.%Comment: 1994 internal repor
Evidential Reasoning with Conditional Belief Functions
In the existing evidential networks with belief functions, the relations
among the variables are always represented by joint belief functions on the
product space of the involved variables. In this paper, we use conditional
belief functions to represent such relations in the network and show some
relations of these two kinds of representations. We also present a propagation
algorithm for such networks. By analyzing the properties of some special
evidential networks with conditional belief functions, we show that the
reasoning process can be simplified in such kinds of networks.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
Knowledge Acquisition, Representation \& Manipulation in Decision Support Systems
In this paper we present a methodology and discuss some implementation issues
for a project on statistical/expert approach to data analysis and knowledge
acquisition. We discuss some general assumptions underlying the project.
Further, the requirements for a user-friendly computer assistant are specified
along with the nature of tools aiding the researcher. Next we show some aspects
of belief network approach and Dempster-Shafer (DST) methodology introduced in
practice to system SEAD. Specifically we present the application of DS
methodology to belief revision problem. Further a concept of an interface to
probabilistic and DS belief networks enabling a user to understand the
communication with a belief network based reasoning system is presentedComment: Intelligent Information Systems Proceedings of a Workshop held in
August\'ow, Poland, 7-11 June, 1993, pages 210- 23
Belief Revision in Probability Theory
In a probability-based reasoning system, Bayes' theorem and its variations
are often used to revise the system's beliefs. However, if the explicit
conditions and the implicit conditions of probability assignments `me properly
distinguished, it follows that Bayes' theorem is not a generally applicable
revision rule. Upon properly distinguishing belief revision from belief
updating, we see that Jeffrey's rule and its variations are not revision rules,
either. Without these distinctions, the limitation of the Bayesian approach is
often ignored or underestimated. Revision, in its general form, cannot be done
in the Bayesian approach, because a probability distribution function alone
does not contain the information needed by the operation.Comment: Appears in Proceedings of the Ninth Conference on Uncertainty in
Artificial Intelligence (UAI1993
Generalized Variational Inference: Three arguments for deriving new Posteriors
We advocate an optimization-centric view on and introduce a novel
generalization of Bayesian inference. Our inspiration is the representation of
Bayes' rule as infinite-dimensional optimization problem (Csiszar, 1975;
Donsker and Varadhan; 1975, Zellner; 1988). First, we use it to prove an
optimality result of standard Variational Inference (VI): Under the proposed
view, the standard Evidence Lower Bound (ELBO) maximizing VI posterior is
preferable to alternative approximations of the Bayesian posterior. Next, we
argue for generalizing standard Bayesian inference. The need for this arises in
situations of severe misalignment between reality and three assumptions
underlying standard Bayesian inference: (1) Well-specified priors, (2)
well-specified likelihoods, (3) the availability of infinite computing power.
Our generalization addresses these shortcomings with three arguments and is
called the Rule of Three (RoT). We derive it axiomatically and recover existing
posteriors as special cases, including the Bayesian posterior and its
approximation by standard VI. In contrast, approximations based on alternative
ELBO-like objectives violate the axioms. Finally, we study a special case of
the RoT that we call Generalized Variational Inference (GVI). GVI posteriors
are a large and tractable family of belief distributions specified by three
arguments: A loss, a divergence and a variational family. GVI posteriors have
appealing properties, including consistency and an interpretation as
approximate ELBO. The last part of the paper explores some attractive
applications of GVI in popular machine learning models, including robustness
and more appropriate marginals. After deriving black box inference schemes for
GVI posteriors, their predictive performance is investigated on Bayesian Neural
Networks and Deep Gaussian Processes, where GVI can comprehensively improve
upon existing methods.Comment: 103 pages, 23 figures (comprehensive revision of previous version
Identification and Interpretation of Belief Structure in Dempster-Shafer Theory
Mathematical Theory of Evidence called also Dempster-Shafer Theory (DST) is
known as a foundation for reasoning when knowledge is expressed at various
levels of detail. Though much research effort has been committed to this theory
since its foundation, many questions remain open. One of the most important
open questions seems to be the relationship between frequencies and the
Mathematical Theory of Evidence. The theory is blamed to leave frequencies
outside (or aside of) its framework. The seriousness of this accusation is
obvious: (1) no experiment may be run to compare the performance of DST-based
models of real world processes against real world data, (2) data may not serve
as foundation for construction of an appropriate belief model.
In this paper we develop a frequentist interpretation of the DST bringing to
fall the above argument against DST. An immediate consequence of it is the
possibility to develop algorithms acquiring automatically DST belief models
from data. We propose three such algorithms for various classes of belief model
structures: for tree structured belief networks, for poly-tree belief networks
and for general type belief networks.Comment: An internal report 199
Robustness Analysis of Bayesian Networks with Local Convex Sets of Distributions
Robust Bayesian inference is the calculation of posterior probability bounds
given perturbations in a probabilistic model. This paper focuses on
perturbations that can be expressed locally in Bayesian networks through convex
sets of distributions. Two approaches for combination of local models are
considered. The first approach takes the largest set of joint distributions
that is compatible with the local sets of distributions; we show how to reduce
this type of robust inference to a linear programming problem. The second
approach takes the convex hull of joint distributions generated from the local
sets of distributions; we demonstrate how to apply interior-point optimization
methods to generate posterior bounds and how to generate approximations that
are guaranteed to converge to correct posterior bounds. We also discuss
calculation of bounds for expected utilities and variances, and global
perturbation models.Comment: Appears in Proceedings of the Thirteenth Conference on Uncertainty in
Artificial Intelligence (UAI1997
Possibilistic Conditioning and Propagation
We give an axiomatization of confidence transfer - a known conditioning
scheme - from the perspective of expectation-based inference in the sense of
Gardenfors and Makinson. Then, we use the notion of belief independence to
"filter out" different proposal s of possibilistic conditioning rules, all are
variations of confidence transfer. Among the three rules that we consider, only
Dempster's rule of conditioning passes the test of supporting the notion of
belief independence. With the use of this conditioning rule, we then show that
we can use local computation for computing desired conditional marginal
possibilities of the joint possibility satisfying the given constraints. It
turns out that our local computation scheme is already proposed by Shenoy.
However, our intuitions are completely different from that of Shenoy. While
Shenoy just defines a local computation scheme that fits his framework of
valuation-based systems, we derive that local computation scheme from II(,8) =
tI(,8 I a) * II(a) and appropriate independence assumptions, just like how the
Bayesians derive their local computation scheme.Comment: Appears in Proceedings of the Tenth Conference on Uncertainty in
Artificial Intelligence (UAI1994
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