One of the tasks of inequality reasoning is to determine the relationship between two quantities for any given set of qualitative constraints. For implementation of this kind of reasoning, Simmons [1] has provided a technique called the "graph search" (GS). This paper briefly reviews GS technique, then presents an implementation...
The Symbolic Probabilistic Inference (SPI) algorithm was developed by Bruce D' Ambrosio for efficient calculation of prior probabilities in belief nets [2]. Although the complexity of the SPI algorithm compares favorably with other approaches to probabilistic inference [5], its actual running time is still prohibitively long even for moderately sized...
Although much effort has been invested to build applications that support group work, collaborative applications have not found easy success. The cost of adopting and maintaining collaborative applications has prevented their widespread use, especially among small distributed groups. Application developers have had difficulties recognizing the extra effort required by groups...
Probabilistic inference using Bayesian networks is now a well-established approach for reasoning under uncertainty. Among many e ciency-driven tech- niques which have been developed, the Optimal Factoring Problem (OFP) is distinguished for presenting a combinatorial optimization point of view on the problem. The contribution of this thesis is to extend...
While much work has been done in estimating software reliability, little attention is paid to predict reliability as early as at the design time. In this report, we present our initial research results of building an early stage software reliability prediction model.
In Part I, we will first investigate and...
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ACKNOWLEDGMENTS
I wish to especially thank my advisor, Dr . Bruce D
Bayesian networks are used for building intelligent agents that act under uncertainty. They are a compact representation of agents' probabilistic knowledge. A Bayesian network can be viewed as representing a factorization of a full joint probability distribution into the multiplication of a set of conditional probability distributions. Independence of causal...
Logic Sampling, Likelihood Weighting and AIS-BN are three variants of stochastic sampling, one class of approximate inference for Bayesian networks. We summarize the ideas underlying each algorithm and the relationship among them. The results from a set of empirical experiments comparing Logic Sampling, Likelihood Weighting and AIS-BN are presented. We...
This paper investigates the feasibility of compiling the functionality of a decision
theoretic problem solving engine into a set of rules or functionally similar construct.
The decision theoretic engine runs in exponential time, while the rule set runs in
linear time at worst. The main question that will determine the...
Reasoning about any realistic domain always involves a degree of uncertainty.
Probabilistic inference in belief networks is one effective way of reasoning under
uncertainty. Efficiency is critical in applying this technique, and many researchers
have been working on this topic. This thesis is the report of our research in this...