SMC (sequential Monte Carlo) for Bayesian phylogenetics

2014 ◽  
pp. 193-216
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Bayesian Phylogenetics

scholarly journals Effective Online Bayesian Phylogenetics via Sequential Monte Carlo with Guided Proposals

2017 ◽  
Vol 67 (3) ◽  
pp. 490-502 ◽  
Author(s):  
Mathieu Fourment ◽  
Brian C Claywell ◽  
Vu Dinh ◽  
Connor McCoy ◽  
Frederick A Matsen IV ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Bayesian Phylogenetics

Bayesian Estimation of DSGE Models

2015 ◽  
Author(s):  
Edward P. Herbst ◽  
Frank Schorfheide
Keyword(s):  
Monte Carlo ◽  
Sequential Monte Carlo ◽  
Likelihood Function ◽  
Academic Research ◽  
Dynamic Stochastic General Equilibrium ◽  
Computational Techniques ◽  
Dsge Models ◽  
Monte Carlo Techniques ◽  
Dynamic Stochastic ◽  
Theoretical Foundations

Dynamic stochastic general equilibrium (DSGE) models have become one of the workhorses of modern macroeconomics and are extensively used for academic research as well as forecasting and policy analysis at central banks. This book introduces readers to state-of-the-art computational techniques used in the Bayesian analysis of DSGE models. The book covers Markov chain Monte Carlo techniques for linearized DSGE models, novel sequential Monte Carlo methods that can be used for parameter inference, and the estimation of nonlinear DSGE models based on particle filter approximations of the likelihood function. The theoretical foundations of the algorithms are discussed in depth, and detailed empirical applications and numerical illustrations are provided. The book also gives invaluable advice on how to tailor these algorithms to specific applications and assess the accuracy and reliability of the computations. The book is essential reading for graduate students, academic researchers, and practitioners at policy institutions.

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