Estimation and Inference via Bayesian Simulation: An Introduction to Markov Chain Monte Carlo

نویسنده

  • Simon Jackman
چکیده

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Recent Advances in Semiparametric Bayesian Function Estimation

Common nonparametric curve tting methods such as spline smooth ing local polynomial regression and basis function approaches are now well devel oped and widely applied More recently Bayesian function estimation has become a useful supplementary or alternative tool for practical data analysis mainly due to breakthroughs in computerintensive inference via Markov chain Monte Carlo simulation This ...

متن کامل

Markov Chain Monte Carlo Methods for Statistical Inference

These notes provide an introduction to Markov chain Monte Carlo methods and their applications to both Bayesian and frequentist statistical inference. Such methods have revolutionized what can be achieved computationally, especially in the Bayesian paradigm. The account begins by discussing ordinary Monte Carlo methods: these have the same goals as the Markov chain versions but can only rarely ...

متن کامل

New Approaches in 3D Geomechanical Earth Modeling

In this paper two new approaches for building 3D Geomechanical Earth Model (GEM) were introduced. The first method is a hybrid of geostatistical estimators, Bayesian inference, Markov chain and Monte Carlo, which is called Model Based Geostatistics (MBG). It has utilized to achieve more accurate geomechanical model and condition the model and parameters of variogram. The second approach is the ...

متن کامل

MCMC: how do we know when to stop?

1 Introduction Markov chain Monte Carlo (MCMC), which enables estimation in complex models via simulation, is now a widespread and accepted statistical tool, particularly in Bayesian analysis. Here, a distribution of interest, or target distribution, π, is approximated by a simulated chain {x

متن کامل

Bayesian Inference and Markov Chain Monte Carlo Methods Applied to Streamflow Forecasting

In this work we propose a Bayesian approach for the parameter estimation problem of stochastic autoregressive models of order p, AR(p), applied to the streamflow forecasting problem. Procedures for model selection, forecasting and robustness evaluation through Monte Carlo Markov Chain (MCMC) simulation techniques are also presented. The proposed approach is compared with the classical one by Bo...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2000