Sold Out
Book Categories |
This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte Carlo methods are inefficient, approximations are implemented, such that learning remains feasible, albeit non-Bayesian. Topics discussed are; basic concepts about probabilities, graph theory and conditional independence; Bayesian network learning from data; Monte Carlo simulation techniques; and the concept of incomplete data. In order to provide a coherent treatment of matters, thereby helping the reader to gain a thorough understanding of the whole concept of learning Bayesian networks from (in)complete data, this publication combines in a clarifying way all the issues presented in the papers with previously unpublished work.
Login|Complaints|Blog|Games|Digital Media|Souls|Obituary|Contact Us|FAQ
CAN'T FIND WHAT YOU'RE LOOKING FOR? CLICK HERE!!! X
You must be logged in to add to WishlistX
This item is in your Wish ListX
This item is in your CollectionApproximation Methods for Efficient Learning of Bayesian Networks: Volume 168 Frontiers in Artificial Intelligence and Applications
X
This Item is in Your InventoryApproximation Methods for Efficient Learning of Bayesian Networks: Volume 168 Frontiers in Artificial Intelligence and Applications
X
You must be logged in to review the productsX
X
X
Add Approximation Methods for Efficient Learning of Bayesian Networks: Volume 168 Frontiers in Artificial Intelligence and Applications, This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte , Approximation Methods for Efficient Learning of Bayesian Networks: Volume 168 Frontiers in Artificial Intelligence and Applications to the inventory that you are selling on WonderClubX
X
Add Approximation Methods for Efficient Learning of Bayesian Networks: Volume 168 Frontiers in Artificial Intelligence and Applications, This publication offers and investigates efficient Monte Carlo simulation methods in order to realize a Bayesian approach to approximate learning of Bayesian networks from both complete and incomplete data. For large amounts of incomplete data when Monte , Approximation Methods for Efficient Learning of Bayesian Networks: Volume 168 Frontiers in Artificial Intelligence and Applications to your collection on WonderClub |