Parichey Gandhi

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In this paper we present the Dynamic Grow-Shrink Inference-based Markov network learning algorithm (abbreviated DGSIMN), which improves on GSIMN, the state-ofthe-art algorithm for learning the structure of the Markov network of a domain from independence tests on data. DGSIMN, like other independence-based algorithms, works by conducting a series of(More)
In this thesis we address the problem of leaning Markov network structure from data by presenting the Dynamic GSIMN or DGSIMN algorithm. DGSIMN is an extension of GSIMN algorithm, and works by conducting a series of statistical conditional independence tests on the data, and uses the axioms that govern the independence relation to avoid unnecessary tests(More)
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