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Bayesian Networks Handbook
ISBN-13: 9781632400758 / Angielski / Twarda / 2015 / 126 str.
A Bayesian network is also known as a Bayes network, belief network or causal probabilistic network. Bayesian belief networks are effective tools to incorporate different information sources with varying levels of uncertainty in a mathematically secure and calculatively effective way. A Bayesian network is a graphical model that ciphers probabilistic relationships among variables of interest. This graphical paradigm has a few significant advantages: firstly, due to the dependencies among all the variables, missing nodes data is also compensated; secondly, belief network sets up the simple relationships and it is easier to identify problematic areas and consequences; thirdly, it has both casual and probabilistic semantics; and lastly, this method along with statistical method provides efficient and balanced approach to avoid over fitting of data. This book analytically and comprehensively describes various aspects of Bayesian networks which will be of great help to students, researchers and professionals in various fields which utilize applications of this model system.
A Bayesian network is also known as a Bayes network, belief network or causal probabilistic network. Bayesian belief networks are effective tools to incorporate different information sources with varying levels of uncertainty in a mathematically secure and calculatively effective way. A Bayesian network is a graphical model that ciphers probabilistic relationships among variables of interest. This graphical paradigm has a few significant advantages: firstly, due to the dependencies among all the variables, missing nodes data is also compensated; secondly, belief network sets up the simple relationships and it is easier to identify problematic areas and consequences; thirdly, it has both casual and probabilistic semantics; and lastly, this method along with statistical method provides efficient and balanced approach to avoid over fitting of data. This book analytically and comprehensively describes various aspects of Bayesian networks which will be of great help to students, researchers and professionals in various fields which utilize applications of this model system.
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