Graphical models for probabilistic and causal reasoning
J Pearl - Quantified representation of uncertainty and …, 1998 - Springer
/ '" '" / Page 1 JUDEA PEARL GRAPHICAL MODELS FOR PROBABILISTIC AND CAUSAL
REASONING 1 INTRODUCTION This chapter surveys the development of graphical models …
REASONING 1 INTRODUCTION This chapter surveys the development of graphical models …
Bayesian networks in environmental and resource management
This overview article for the special series,“Bayesian Networks in Environmental and
Resource Management,” reviews 7 case study articles with the aim to compare Bayesian …
Resource Management,” reviews 7 case study articles with the aim to compare Bayesian …
[图书][B] Probabilistic graphical models: principles and techniques
D Koller, N Friedman - 2009 - books.google.com
A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …
would enable a computer to use available information for making decisions. Most tasks …
[图书][B] Probabilistic reasoning in intelligent systems: networks of plausible inference
J Pearl - 2014 - books.google.com
Probabilistic Reasoning in Intelligent Systems is a complete and accessible account of the
theoretical foundations and computational methods that underlie plausible reasoning under …
theoretical foundations and computational methods that underlie plausible reasoning under …
Vines--a new graphical model for dependent random variables
T Bedford, RM Cooke - The Annals of Statistics, 2002 - projecteuclid.org
A new graphical model, called a vine, for dependent random variables is introduced. Vines
generalize the Markov trees often used in modelling high-dimensional distributions. They …
generalize the Markov trees often used in modelling high-dimensional distributions. They …
[图书][B] Probabilistic networks and expert systems: Exact computational methods for Bayesian networks
Winner of the 2002 DeGroot Prize. Probabilistic expert systems are graphical networks that
support the modelling of uncertainty and decisions in large complex domains, while …
support the modelling of uncertainty and decisions in large complex domains, while …
[图书][B] Probabilistic risk analysis: foundations and methods
T Bedford, R Cooke - 2001 - books.google.com
" Probabilistic risk analysis aims to quantify the risk caused by high technology installations.
Increasingly, such analyses are being applied to a wider class of systems in which problems …
Increasingly, such analyses are being applied to a wider class of systems in which problems …
[图书][B] Systems thinking, critical realism and philosophy: A confluence of ideas
J Mingers - 2014 - taylorfrancis.com
Systems Thinking, Critical Realism and Philosophy: A Confluence of Ideas seeks to re-
address the whole question of philosophy and systems thinking for the twenty first century …
address the whole question of philosophy and systems thinking for the twenty first century …
The EM algorithm for graphical association models with missing data
SL Lauritzen - Computational statistics & data analysis, 1995 - Elsevier
It is shown how the computational scheme of Lauritzen and Spiegelhalter (1988) can be
exploited to perform the E-step of the EM algorithm when applied to finding maximum …
exploited to perform the E-step of the EM algorithm when applied to finding maximum …
Guidelines for developing and updating Bayesian belief networks applied to ecological modeling and conservation
BG Marcot, JD Steventon… - Canadian Journal of …, 2006 - cdnsciencepub.com
Bayesian belief networks (BBNs) are useful tools for modeling ecological predictions and
aiding resource-management decision-making. We provide practical guidelines for …
aiding resource-management decision-making. We provide practical guidelines for …