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Filtering by: Creator Kapuscinski, Pearl B. Remove constraint Creator: Kapuscinski, Pearl B. Degree Field Computer Science Remove constraint Degree Field: Computer Science

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  • Learning cost-sensitive diagnostic policies from data

    Creator:
    Zubek, Valentina Bayer
    Abstract:
    In its simplest form, the process of diagnosis is a decision-making process in which the diagnostician performs a sequence of tests culminating in a diagnostic decision. For example, a physician might perform a series of simple measurements (body tem- perature, weight, etc.) and laboratory measurements (white blood count, CT scan,...
    Resource Type:
    Dissertation
    Full Text:
    performing various diagnostic tests) and (b) misdiagnosis costs (the costs incurred when the patient is

  • 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

  • Causal Structure Learning from Experiments and Observations

    Creator:
    Sinha, Meghamala
    Abstract:
    In this research, we address the problem of learning a single causal network structure from multiple dataset generated from different experiments. The experiments can be observational or interventional. We assume that each dataset is generated by an unknown causal network altered under different experimental conditions (interventions, manipulation or perturbation). As...
    Resource Type:
    Masters Thesis
    Full Text:
    . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 5.1 (a) Classic signaling network with points of interventions by reagents, (b) Model inference

  • 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

  • Personalizing machine learning systems with explanatory debugging

    Creator:
    Kulesza, Todd
    Abstract:
    How can end users efficiently influence the predictions that machine learning systems make on their behalf? Traditional systems rely on users to provide examples of how they want the learning system to behave, but this is not always practical for the user, nor efficient for the learning system. This dissertation...
    Resource Type:
    Dissertation

  • Analytical Methods for Improving Causal Inference

    Creator:
    Sinha, Meghamala
    Abstract:
    Causal inference is an important analytical tool to bridge the gap between prediction and decision-making. However, learning a causal network solely from data is a challenging task. In this work, various techniques have been explored for a better and improved causal network learning from data. Firstly, the problem of learning...
    Resource Type:
    Dissertation
    Full Text:
    a red arrow be- tween Vi and Vj), and false negative edges (shown in (b) as blue arrows between Vi

  • Dependent evidence in reasoning with uncertainty

    Creator:
    Ling, Xiaoning
    Abstract:
    The problem of handling dependent evidence is an important practical issue for applications of reasoning with uncertainty in artificial intelligence. The existing solutions to the problem are not satisfactory because of their ad hoc nature, complexities, or limitations. In this dissertation, we develop a general framework that can be used...
    Resource Type:
    Dissertation
    Full Text:
    which deal with uncertain information such as Pearl's belief networks [Pearl 1986] and Shafer's Markov