Probabilistic graphical models : principles and applications / Luis Enrique Sucar.
Tipo de material:
TextoSeries Advances in Computer Vision and Pattern RecognitionEditor: London : Springer London : Springer, 2015Descripción: xxiv, 253 páginas : 117 ilustraciones, 4 ilustraciones en colorTipo de contenido: - texto
- computadora
- recurso en línea
- 9781447166993
- QA276-280
Contenidos:
Part I: Fundamentals -- Introduction -- Probability Theory -- Graph Theory -- Part II: Probabilistic Models -- Bayesian Classifiers -- Hidden Markov Models -- Markov Random Fields -- Bayesian Networks: Representation and Inference -- Bayesian Networks: Learning -- Dynamic and Temporal Bayesian Networks -- Part III: Decision Models -- Decision Graphs -- Markov Decision Processes -- Part IV: Relational and Causal Models -- Relational Probabilistic Graphical Models -- Graphical Causal Models.
No hay ítems correspondientes a este registro
Springer eBooks
Part I: Fundamentals -- Introduction -- Probability Theory -- Graph Theory -- Part II: Probabilistic Models -- Bayesian Classifiers -- Hidden Markov Models -- Markov Random Fields -- Bayesian Networks: Representation and Inference -- Bayesian Networks: Learning -- Dynamic and Temporal Bayesian Networks -- Part III: Decision Models -- Decision Graphs -- Markov Decision Processes -- Part IV: Relational and Causal Models -- Relational Probabilistic Graphical Models -- Graphical Causal Models.
Para consulta fuera de la UANL se requiere clave de acceso remoto.