Search Results - subject:"sequential monte carlo"

Bayesian Estimation of Wishart Autoregressive Stochastic Volatility Model

Lin, Ming; Liu, Changjiang; Niu, Linlin - 2013

The Wishart autoregressive (WAR) process is a powerful tool to model multivariate stochastic volatility (MSV) with correlation risk and derive closed-form solutions in various asset pricing models. However, making inferences of the WAR stochastic volatility (WAR-SV) model is challenging because...

Persistent link: https://www.econbiz.de/10010892135

Sequential estimation of mixtures of structured autoregressive models

Prado, Raquel - In: Computational Statistics & Data Analysis 58 (2013) C, pp. 58-70

sequential Monte Carlo algorithm. The performance of such algorithm is illustrated by applying it to simulated data and EEG data …

Persistent link: https://www.econbiz.de/10010871349

Sequential Monte Carlo for Bayesian sequentially designed experiments for discrete data

Drovandi, Christopher C.; McGree, James M.; Pettitt, … - In: Computational Statistics & Data Analysis 57 (2013) 1, pp. 320-335

In this paper we present a sequential Monte Carlo algorithm for Bayesian sequential experimental design applied to …

Persistent link: https://www.econbiz.de/10010871482

Adaptive approximate Bayesian computation for complex models

Lenormand, Maxime; Jabot, Franck; Deffuant, Guillaume - In: Computational Statistics 28 (2013) 6, pp. 2777-2796

We propose a new approximate Bayesian computation (ABC) algorithm that aims at minimizing the number of model runs for reaching a given quality of the posterior approximation. This algorithm automatically determines its sequence of tolerance levels and makes use of an easily interpretable...

Persistent link: https://www.econbiz.de/10010847820

Inference for a class of partially observed point process models

Martin, James; Jasra, Ajay; McCoy, Emma - In: Annals of the Institute of Statistical Mathematics 65 (2013) 3, pp. 413-437

Monte Carlo methods, investigating the problems of performing sequential filtering and smoothing in complex examples, where … process models with static parameters. Taking on a Bayesian perspective for the static parameters, we build upon sequential …

Persistent link: https://www.econbiz.de/10010848646

Computational aspects of Bayesian spectral density estimation

Liseo, Brunero; Rousseau, Judith; Chopin, Nicolas - Université Paris-Dauphine (Paris IX) - 2013

-modal, and derive a Sequential Monte Carlo sampler based on an annealing sequence in order to sample from the approximate …

Persistent link: https://www.econbiz.de/10010960570

Particle algorithms for optimization on binary spaces

Schäfer, Christian - Université Paris-Dauphine (Paris IX) - 2013

We propose a general sequential Monte Carlo approach for optimization of pseudo-Boolean objective functions. There are … sequential Monte Carlo techniques, including the cross-entropy method and simulated annealing as special cases. Secondly, we …

Persistent link: https://www.econbiz.de/10011072765

Sequential Monte Carlo on large binary sampling spaces

Schäfer, Christian; Chopin, Nicolas - Université Paris-Dauphine (Paris IX) - 2013

posterior distribution on the model space using an appropriate version of Sequential Monte Carlo. Raw versions of Sequential … provide a review of models for binary data and make one of them work in the context of Sequential Monte Carlo sampling … Monte Carlo are easily implemented using binary vectors with independent components. For high-dimensional problems, however …

Persistent link: https://www.econbiz.de/10011074311

Tuned iterated filtering

Lindström, Erik - In: Statistics & Probability Letters 83 (2013) 9, pp. 2077-2080

Iterated filtering is an algorithm for estimating parameters in partially observed Markov process (POMP) models. The real-world performance of the algorithm depends on several tuning parameters. We propose a simple method for optimizing the parameter governing the joint dynamics of the hidden...

Persistent link: https://www.econbiz.de/10011040011

Likelihood computation for hidden Markov models via generalized two-filter smoothing

Persing, Adam; Jasra, Ajay - In: Statistics & Probability Letters 83 (2013) 5, pp. 1433-1442

We introduce an estimate for the likelihood of hidden Markov models (HMMs) using sequential Monte Carlo (SMC …

Persistent link: https://www.econbiz.de/10011040015