There are three families of exact methods used for probabilistic inference in
belief nets. It is necessary to compare them and analyze the advantages and
the disadvantages of each algorithm, and know the time cost of making
inferences in a given belief network. This paper discusses the factors that
influence...
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...
I present a new heuristic search approach to compute approximate answers for the probability query in belief nets. This approach can compute the 'best' bounds for a query in a period of any given time (if time permitted, it will get an exact value). It inherits the essence of Symbolic...
Probabilistic inference in belief networks provides an effective way of reasoning under uncertainty. Efficiency is critical in applying this technique and many algorithms have been developed by many researchers. This is to report the object oriented design and implementation in C++ of such a probabilistic inference system using efficient algorithms.
The structural and chemical diversity of natural compounds represents a vast amount of potential in terms of new drug discovery. This project examines the cytotoxic potential of the marine natural product mandelalide A and seeks to understand whether or not exposure to this compound induces programmed cell death in cancer...
This Thesis aims to determine whether we can improve the accuracy, resolution, and speed of calculations for common power system problems using simple computational models that scale well to machine learning and high performance computing solutions. The second chapter of this Thesis implements more precise aging and degradation models for...
Cognitive Radio Networks (CRNs) enable opportunistic access to the licensed channel resources by allowing unlicensed users to exploit vacant channel opportunities. One effective technique through which unlicensed users, often referred to as Secondary Users (SUs), acquire whether a channel is vacant is cooperative spectrum sensing. Despite its effectiveness in enabling...
The ever-increasing global population presents looming problems for the field of agriculture. Global food demand will, at some point, increase to the point where there is not enough crop-ready land to keep up. This creates an additional incentive, other than economics, for growers to increase their yield-per-acre and make sure...
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...
Knowledge-Based Model Construction (KBMC) has generated a lot of attention
due to its importance as a technique for generating probabilistic or decision-theoretic
models whose range of applicability in AI has been vastly increased. However, no
one has tried to analyze the essential issues in KBMC, to determine if there exists...