Search Results - subject:"importance sampling."

Efficient likelihood evaluation of state-space representations

DeJong, David Neil; Dharmarajan, Hariharan; Liesenfeld, … - 2009

target densities. Construction is achieved via efficient importance sampling, and approximating densities are adapted to …

Persistent link: https://www.econbiz.de/10010298827

Determinants and dynamics of current account reversals: an empirical analysis

Liesenfeld, Roman; Moura, Guilherme V.; Richard, … - 2009

likelihood-based inference of these models requires high-dimensional integration for which we use Efficient Importance Sampling …

Persistent link: https://www.econbiz.de/10010298829

The Cross-Entropy Method With Patching For Rare-Event Simulation Of Large Markov Chains

Kaynar, Bahar; Ridder, Ad - 2009

There are various importance sampling schemes to estimate rare event probabilities in Markovian systems such as … Markovian reliability models and Jackson networks. In this work, we present a general state dependent importance sampling method … with other importance sampling schemes. The performance of the importance sampling schemes is measured by the relative …

Persistent link: https://www.econbiz.de/10010325747

To Bridge, to Warp or to Wrap? A Comparative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihoods

Ardia, David; Hoogerheide, Lennart; van Dijk, Herman K. - 2009

importance sampling or the independence chain Metropolis-Hastings algorithm for posterior analysis. A comparative analysis is … appropriately yet quickly tuned candidate, straightforward importance sampling provides the most efficient estimator of the marginal …

Persistent link: https://www.econbiz.de/10010325939

Bayesian model comparison based on expected posterior priors for discrete decomposable graphical models

Consonni, Guido; Lupparelli, Monia - 2009

The implementation of the Bayesian paradigm to model comparison can be problematic. In particular, prior distributions on the parameter space of each candidate model require special care. While it is well known that improper priors cannot be used routinely for Bayesian model comparison, we claim...

Persistent link: https://www.econbiz.de/10010335266

Efficient likelihood evaluation of state-space representations

DeJong, David Neil; Dharmarajan, Hariharan; Liesenfeld, … - Institut für Volkswirtschaftslehre, … - 2009

target densities. Construction is achieved via efficient importance sampling, and approximating densities are adapted to …

Persistent link: https://www.econbiz.de/10005059009

Determinants and dynamics of current account reversals: an empirical analysis

Liesenfeld, Roman; Moura, Guilherme V.; Richard, … - Institut für Volkswirtschaftslehre, … - 2009

likelihood-based inference of these models requires high-dimensional integration for which we use Efficient Importance Sampling …

Persistent link: https://www.econbiz.de/10005059013

To Bridge, to Warp or to Wrap? A Comparative Study of Monte Carlo Methods for Efficient Evaluation of Marginal Likelihoods

Ardia, David; Hoogerheide, Lennart; Dijk, Herman K. van - Tinbergen Institute - 2009

use of importance sampling or the independence chain Metropolis-Hastings algorithm for posterior analysis. A comparative … appropriately yet quickly tuned candidate, straightforward importance sampling provides the most efficient estimator of the marginal …

Persistent link: https://www.econbiz.de/10005016276

Learning and filtering via simulation: smoothly jittered particle filters

Shephard, Neil; Flury, Thomas - Department of Economics, Oxford University - 2009

A key ingredient of many particle filters is the use of the sampling importance resampling algorithm (SIR), which transforms a sample of weighted draws from a prior distribution into equally weighted draws from a posterior distribution. We give a novel analysis of the SIR algorithm and analyse...

Persistent link: https://www.econbiz.de/10008497742

Automated Likelihood Based Inference for Stochastic Volatility Models

Skaug, Hans J.; Yu, Jun - School of Economics, Singapore Management University - 2009

In this paper the Laplace approximation is used to perform classical and Bayesian analyses of univariate and multivariate stochastic volatility (SV) models. We show that implementation of the Laplace approximation is greatly simplified by the use of a numerical technique known as automatic...

Persistent link: https://www.econbiz.de/10008521815