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Filtering by: Creator Kapuscinski, Pearl B. Remove constraint Creator: Kapuscinski, Pearl B. Advisor d'ambrosio, Bruce Remove constraint Advisor: d'ambrosio, Bruce

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  • Heuristic search in symbolic probability inference

    Creator:
    Pan, Yonghong
    Abstract:
    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...
    Resource Type:
    Capstone Project
    Full Text:
    reduction algorithm [Shachter, 1986, 1989], and Pearl's message passing algorithm [Pearl,1988]. Both of

  • Object oriented design and implementation of a probabilistic inference system

    Creator:
    Yan, Caihua
    Abstract:
    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.
    Resource Type:
    Capstone Project
    Full Text:
    (a): p(a=l)=0.2 p(b): p(b=l)=0.3 p(cla): p(c=lla=l)=0.8, p(c=lla=0)=0.3 p(dla,b): p(d=lla=l,b=l

  • Optimal inference with local expressions

    Creator:
    Borujerdi, Mohammad
    Abstract:
    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...
    Resource Type:
    Dissertation
    Full Text:
    dependence of D on A and B, following Pearl (Pearl, 1988): P (D = t) = 1� (1� cA(D))(1� cB(D)) P (D = f

  • Complexity of probabilistic inference in belief nets--an experimental study

    Creator:
    Li, Zhaoyu
    Abstract:
    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...
    Resource Type:
    Masters Thesis
    Full Text:
    , Pearl [6,7] developed an extended representation for belief networks which adds two distributions

  • Representations and algorithms for efficient inference in Bayesian networks

    Creator:
    Takikawa, Masami
    Abstract:
    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...
    Resource Type:
    Dissertation
    Full Text:
    conditional probabilities that must be specified. A Bayesian network [Pearl, 1988] is a directed acyclic

  • Logic sampling, likelihood weighting and AIS-BN : an exploration of importance sampling

    Creator:
    Wang, Haiou
    Abstract:
    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...
    Resource Type:
    Masters Thesis
    Full Text:
    the ICPT tables of the 10 Burglary P(B) 0.001 Alarm Earthquake B P(A) T T 095 T F 0.94 F T

  • A factoring approach for probabilistic inference in belief networks

    Creator:
    Li, Zhaoyu
    Abstract:
    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...
    Resource Type:
    Dissertation
    Full Text:
    MPE for nodes b and c. 89 10. The evaluation tree of querying p(e) given f = 1 in the simple belief

  • Knowledge-based decision model construction for hierarchical diagnosis and repair

    Creator:
    Yuan, Soe-Tsyr
    Abstract:
    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...
    Resource Type:
    Dissertation
    Full Text:
    B which indicates "A makes B more likely". (b) A qualitative relationship between A and B which

  • Parallelism in the SPI algorithm : an investigation

    Creator:
    Fountain, Tony
    Abstract:
    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...
    Resource Type:
    Capstone Project
    Full Text:
    for P(A) . 5 2.3 Subtree of evaluation tree 7 2.4 Distributions 7 2.5 P(B IE) ... 8 3.1

  • Analysis of Bayesian anytime inference algorithms

    Creator:
    Burgess, Scott Alan
    Abstract:
    This dissertation explores and analyzes the performance of several Bayesian anytime inference algorithms for dynamic influence diagrams. These algorithms are compared on the On-Line Maintenance Agent testbed, a software artifact permitting comparison of dynamic reasoning algorithms used by an agent on a variety of simulated maintenance and monitoring tasks. Analysis...
    Resource Type:
    Dissertation
    Full Text:
    ................................................. 100 Appendix B: Raw Data for Experiments with the Peaked Backward Simulation Algorithm