Probabilistic Inference Using Markov Chain Monte Carlo Methods

Probabilistic Inference Using Markov Cha

📁 Probabilistic Inference Using Markov Chain Monte Carlo Methods

✍ Neal R.M. 📂 Library 📅 1993 🏛 U. Toronto 🌐 English

Probabilistic Inference Using Markov Cha

📁 Probabilistic Inference Using Markov Chain Monte Carlo Methods

✍ Neal R. 📂 Library 📅 1993 🌐 English

In this review, I outline the role of probabilistic inference in artificial intelligence, present the theory of Markov chains, and describe various Markov chain Monte-Carlo algorithms, along with a number of supporting techniques. I try to present a comprehensive picture of the range of methods that

Advanced Markov chain Monte Carlo method

📁 Advanced Markov chain Monte Carlo methods

✍ Faming Liang, Chuanhai Liu, Raymond Carroll 📂 Library 📅 2010 🏛 Wiley 🌐 English

Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bi

Markov Chain Monte Carlo: Stochastic Sim

📁 Markov Chain Monte Carlo: Stochastic Simulation for Bayesian Inference

✍ Dani Gamerman, Hedibert Freitas Lopes 📂 Library 📅 2006 🏛 Chapman and Hall/CRC 🌐 English

While there have been few theoretical contributions on the Markov Chain Monte Carlo (MCMC) methods in the past decade, current understanding and application of MCMC to the solution of inference problems has increased by leaps and bounds. Incorporating changes in theory and highlighting new applicati

Markov chain Monte Carlo : stochastic si

📁 Markov chain Monte Carlo : stochastic simulation for Bayesian inference

✍ Dani Gamerman 📂 Library 📅 2006 🏛 Chapman & Hall 🌐 English

Advanced Markov Chain Monte Carlo Method

📁 Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples

✍ Faming Liang; Chuanhai Liu; Raymond Carroll 📂 Library 📅 2011 🏛 Wiley 🌐 English

Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific computing. This book discusses recent developments of MCMC methods with an emphasis on those making use of past sample information during simulations. The application examples are drawn from diverse fields such as bi