Bayesian Methods For Neural Networks - Theory and Application

Bayesian Learning for Neural Networks

✍ Radford M. Neal (auth.) 📂 Library 📅 1996 🏛 Springer-Verlag New York 🌐 English

Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network

Bayesian Learning for Neural Networks

📁 Bayesian Learning for Neural Networks

✍ Radford M. Neal 📂 Library 📅 1995 🌐 English

Bayesian Theory and Methods with Applica

📁 Bayesian Theory and Methods with Applications

✍ Vladimir Savchuk, Chris P. Tsokos (auth.) 📂 Library 📅 2011 🏛 Atlantis Press 🌐 English

Bayesian methods are growing more and more popular, finding new practical applications in the fields of health sciences, engineering, environmental sciences, business and economics and social sciences, among others. This book explores the use of Bayesian analysis in the statistical estimation

Artificial Neural Networks: Methods and

📁 Artificial Neural Networks: Methods and Applications

✍ Hugh M. Cartwright B.Sc, PhD (auth.), David J. Livingstone (eds.) 📂 Library 📅 2009 🏛 Humana Press 🌐 English

As an extension of artificial intelligence research, artificial neural networks (ANN) aim to simulate intelligent behavior by mimicking the way that biological neural networks function. In Artificial Neural Networks, an international panel of experts report the history of the applicat

Innovations in Bayesian Networks: Theory

📁 Innovations in Bayesian Networks: Theory and Applications

✍ Dawn E. Holmes, Lakhmi C. Jain (auth.), Prof. Dawn E. Holmes, Prof. Lakhmi C. Ja 📂 Library 📅 2008 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained.<

Innovations in Bayesian Networks: Theory

📁 Innovations in Bayesian Networks: Theory and Applications

✍ Dawn E. Holmes, Lakhmi C. Jain (auth.), Prof. Dawn E. Holmes, Prof. Lakhmi C. Ja 📂 Library 📅 2008 🏛 Springer-Verlag Berlin Heidelberg 🌐 English

Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained.<