Zobrazeno 1 - 10
of 3 432
pro vyhledávání: '"Variable-order Bayesian network"'
Autor:
Joseph E. Cavanaugh, Andrew A. Neath
Publikováno v:
Journal of Data Science. 4:131-146
Consider the problem of selecting independent samples from several populations for the purpose of between-group comparisons. An im- portant aspect of the solution is the determination of clusters where mean levels are equal, often accomplished using
Publikováno v:
Journal of Data Science. 10:51-59
Interval estimation for the proportion parameter in one-sample misclassied binary data has caught much interest in the literature. Re- cently, an approximate Bayesian approach has been proposed. This ap- proach is simpler to implement and performs be
Publikováno v:
Management Science. 67:1622-1638
In standard models of iterative thinking, players choose a fixed rule level from a fixed rule hierarchy. Nonequilibrium behavior emerges when players do not perform enough thinking steps. Existing approaches, however, are inherently static. This pape
Autor:
José A. Gámez, Arcadio Rubio
Publikováno v:
GECCO '11: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation
GECCO '11: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, Jul 2011, Dublin, Ireland. pp.1219, ⟨10.1145/2001576.2001741⟩
GECCO
GECCO '11: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation, Jul 2011, Dublin, Ireland. pp.1219, ⟨10.1145/2001576.2001741⟩
GECCO
International audience; In this paper we present an extension to the classical k-dependence Bayesian network classifier algorithm. The original method intends to work for the whole continuum of Bayesian classifiers, from naïve Bayes to unrestricted
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::72d0adafbb2ec8c3248364c6302e68df
https://ora.ox.ac.uk/objects/uuid:c48bea52-9a4b-4b8a-87d5-50b4658bd116
https://ora.ox.ac.uk/objects/uuid:c48bea52-9a4b-4b8a-87d5-50b4658bd116
Publikováno v:
Frontiers in Robotics, Automation and Control
1.1 Motivation A fundamental problem encountered in many fields is to model data t o given a discrete time-series data sequence ( ) T o o : y , ,L 1 = . This problem is found in diverse fields, such as control systems, robotics, event detection (Moto
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b90c2b400ed71d8a5de34193b6206c5
http://www.intechopen.com/articles/show/title/a_hierarchical_bayesian_hidden_markov_model_for_multi-dimensional_discrete_data
http://www.intechopen.com/articles/show/title/a_hierarchical_bayesian_hidden_markov_model_for_multi-dimensional_discrete_data
Autor:
Ferat Sahin, Archana Devasia
Publikováno v:
Swarm Intelligence, Focus on Ant and Particle Swarm Optimization
Particle Swarm Optimization (PSO) was first introduced as a concept for a non-linear optimizer by Kennedy and Eberhart in 1995. Their seminal work articulates a technique of evolutionary computation, which has its origin in artificial intelligence an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::051fb5c6de65a9a8b189e874c0e5756b
http://www.intechopen.com/articles/show/title/distributed_particle_swarm_optimization_for_structural_bayesian_network_learning
http://www.intechopen.com/articles/show/title/distributed_particle_swarm_optimization_for_structural_bayesian_network_learning
Publikováno v:
Bayesian Network
In this chapter, we described the Implicit method, a new framework for learning structure and probabilities in Bayesian networks. We showed how our method proceeds with complete and incomplete data. The use of the Implicit method was illustrated on a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d69a57c6518af94589ca9b978f16f326
http://www.intechopen.com/articles/show/title/learning-structure-and-parameters-in-bayesian-networks-using-an-implicit-framework
http://www.intechopen.com/articles/show/title/learning-structure-and-parameters-in-bayesian-networks-using-an-implicit-framework
Autor:
Yongqun He, Zuoshuang Xiang
Publikováno v:
Bayesian Network
In this chapter, we have introduced in details the miniTUBA system, and how to apply the miniTUBA dynamic Bayesian network (DBN) approach to analyze a typical use case in the areas of host-pathogen interactions using high throughput microarray data.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0055cf8bf93af2c49ef75250d32ac2d9
http://www.intechopen.com/articles/show/title/minituba-a-web-based-dynamic-bayesian-network-analysis-system-
http://www.intechopen.com/articles/show/title/minituba-a-web-based-dynamic-bayesian-network-analysis-system-
Autor:
Luis Enrique Sucar
Publikováno v:
Probabilistic Graphical Models ISBN: 9783030619428
Probabilistic Graphical Models ISBN: 9781447166986
Probabilistic Graphical Models ISBN: 9781447166986
This chapter gives an introduction to learning Bayesian networks including both parameter and structure learning. Parameter learning includes how to handle uncertainty in the parameters and missing data; it also includes the basic discretization tech
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d6c073bda419a959aee1803e3d6e0177
https://doi.org/10.1007/978-3-030-61943-5_8
https://doi.org/10.1007/978-3-030-61943-5_8
Autor:
Luis Enrique Sucar
Publikováno v:
Probabilistic Graphical Models ISBN: 9783030619428
Probabilistic Graphical Models ISBN: 9781447166986
Probabilistic Graphical Models ISBN: 9781447166986
This chapter introduces Bayesian networks, covering representation and inference. The basic representational aspects of a Bayesian network are presented, including the concept of D-Separation and the independence axioms. With respect to parameter spe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4084f235827bf256a1c9f010533f45e5
https://doi.org/10.1007/978-3-030-61943-5_7
https://doi.org/10.1007/978-3-030-61943-5_7