Modelos de redes bayesianas con variables discretas y continuas
This book delves into Bayesian network models featuring both discrete and continuous variables. It covers model construction, learning algorithms, and inference methods, providing tools for handling uncertainty in diverse applications. Ideal for those advancing in probabilistic modeling.
About This Book
Bayesian networks offer a powerful framework for representing and reasoning with uncertainty in various domains. This book focuses on models that integrate discrete and continuous variables, enabling more flexible representations of real-world phenomena.
The author presents methodologies for building these hybrid models, addressing challenges in structure learning and parameter estimation. Techniques are discussed for handling mixed variable types effectively within the Bayesian paradigm.
Practical examples illustrate the application of these models in inference tasks. The content is geared toward researchers and practitioners seeking to apply probabilistic graphical models in their work.
Emphasis is placed on computational aspects, including algorithms for exact and approximate inference in networks with continuous components.
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