List Price : $75.00
You Save : Check Lowest Price !!!
Availibility : Usually ships in 24 hours
Although you may perhaps currently have a Learning in Graphical Models (Adaptive Computation and Machine Learning) we would be willing to wager that the latest product is like nothing you have ever come across before. When you acquire one of these, you are searching for something which is going to make your daily life a little easier and when it does not take place, you're certain to be unhappy. Manufacturer has gone to amazing lengths to make sure that you're pleased with our newest style.
Although manufacturer will not be an original maker of the Learning in Graphical Models (Adaptive Computation and Machine Learning) what we did is taken a good suggestion and made it into a great product that will give several years of exceptional services. Our primary model enticed several buyers because it was developed to the top standards of quality. We have carried on this tradition to bring you our very newest edition of a tried and trustworthy product.
Another thing we're also certain of is you will find that at this value, you're not going to find a superior Learning in Graphical Models (Adaptive Computation and Machine Learning) anywhere. To ensure that that you are actually getting true value for your cash manufacturer has built-in numerous really nice options that are sure to make this among the items you have been able to discover in several years. We are sure that you are going to feel as if you are getting genuine value for your cash when you realize all the additional touches We have built into our latest version for making your daily life much better.
Graphical models, a marriage between probability theory and graph theory, provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering--uncertainty and complexity. In particular, they play an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity: a complex system is built by combining simpler parts. Probability theory serves as the glue whereby the parts are combined, ensuring that the system as a whole is consistent and providing ways to interface models to data. Graph theory provides both an intuitively appealing interface by which humans can model highly interacting sets of variables and a data structure that lends itself naturally to the design of efficient general-purpose algorithms.This book presents an in-depth exploration of issues related to learning within the graphical model formalism. Four chapters are tutorial chapters--Robert Cowell on Inference for Bayesian Networks, David MacKay on Monte Carlo Methods, Michael I. Jordan et al. on Variational Methods, and David Heckerman on Learning with Bayesian Networks. The remaining chapters cover a wide range of topics of current research interest.
undefined
No comments:
Post a Comment