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    Latent Structure And Causality: Inference From Data

    Latent Structure And Causality: Inference From Data by Zhou, Qing;

      • GET 8% OFF

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      • Publisher's listprice GBP 80.00
      • The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.

        40 488 Ft (38 560 Ft + 5% VAT)
      • Discount 8% (cc. 3 239 Ft off)
      • Discounted price 37 249 Ft (35 475 Ft + 5% VAT)

    40 488 Ft

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    Product details:

    • Publisher World Scientific
    • Date of Publication 17 April 2025

    • ISBN 9789811290688
    • Binding Hardback
    • No. of pages288 pages
    • Language English
    • 700

    Categories

    Long description:

    Inferring latent structure and causality is crucial for understanding underlying patterns and relationships hidden in the data. This book covers selected models for latent structures and causal networks and inference methods for these models.After an introduction to the EM algorithm on incomplete data, the book provides a detailed coverage of a few widely used latent structure models, including mixture models, hidden Markov models, and stochastic block models. EM and variation EM algorithms are developed for parameter estimation under these models, with comparison to their Bayesian inference counterparts. We make further extensions of these models to related problems, such as clustering, motif discovery, Kalman filtering, and exchangeable random graphs. Conditional independence structures are utilized to infer the latent structures in the above models, which can be represented graphically. This notion generalizes naturally to the second part on graphical models that use graph separation to encode conditional independence. We cover a variety of graphical models, including undirected graphs, directed acyclic graphs (DAGs), chain graphs, and acyclic directed mixed graphs (ADMGs), and various Markov properties for these models. Recent methods that learn the structure of a graphical model from data are reviewed and discussed. In particular, DAGs and Bayesian networks are an important class of mathematical models for causality. After an introduction to causal inference with DAGs and structural equation models, we provide a detailed review of recent research on causal discovery via structure learning of graphs. Finally, we briefly introduce the causal bandit problem with sequential intervention.

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