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Estimation and Optimization of Functions
Charles J. Geyer
- pp 241-258
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The article was published on 1996-01-01 and is currently open access. It has received 103 citations till now. The article focuses on the topics: Continuous optimization & Discrete optimization.read more
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Contemporary Bayesian Econometrics and Statistics
TL;DR: In this article, the authors proposed a Bayesian inference method based on the prior distribution of the probability distributions of the classes of a set of classes in a class with respect to the probability distribution of each class.
Journal ArticleDOI
MCMC Analysis of Diffusion Models With Application to Finance
TL;DR: In this article, a Markov chain Monte Carlo (MCMCMC) method is proposed for estimating the parameters of diffusion processes from discrete observations. But the method is not suitable for a wide class of models including systems with unobservable state variables and nonlinearities.
Journal ArticleDOI
Fitting population models incorporating process noise and observation error
Perry de Valpine,Alan Hastings +1 more
TL;DR: The numerically integrated state-space (NISS) method as mentioned in this paper was proposed to fit models to time series of population abun- dances that incorporate both process noise and observation error in a likelihood framework.
Journal ArticleDOI
Inference in molecular population genetics
Matthew Stephens,Peter Donnelly +1 more
TL;DR: This work introduces a new IS algorithm that substantially outperforms existing IS algorithms, with efficiency typically improved by several orders of magnitude, and compares favourably with existing MCMC methods in some problems, and less favourably in others, suggesting that both IS andMCMC methods have a continuing role to play in this area.
Journal ArticleDOI
State-space models for the dynamics of wild animal populations
TL;DR: In this article, a unified framework for jointly defining population dynamics models and measurements taken on a population is developed, where the population processes are modelled by the state process and measurements are modeled by the observation process.