Following a Moving Target—Monte Carlo Inference for Dynamic Bayesian Models
| العنوان: | Following a Moving Target—Monte Carlo Inference for Dynamic Bayesian Models |
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| المؤلفون: | Carlo Berzuini, Walter R. Gilks |
| المصدر: | Journal of the Royal Statistical Society Series B: Statistical Methodology. 63:127-146 |
| بيانات النشر: | Oxford University Press (OUP), 2001. |
| سنة النشر: | 2001 |
| مصطلحات موضوعية: | Statistics and Probability, Markov chain, Monte Carlo method, Sampling (statistics), Markov chain Monte Carlo, Bayesian inference, Statistics::Computation, symbols.namesake, ComputingMethodologies_PATTERNRECOGNITION, Resampling, Statistics, symbols, Statistics, Probability and Uncertainty, Particle filter, Algorithm, Importance sampling, Mathematics |
| الوصف: | Summary Markov chain Monte Carlo (MCMC) sampling is a numerically intensive simulation technique which has greatly improved the practicality of Bayesian inference and prediction. However, MCMC sampling is too slow to be of practical use in problems involving a large number of posterior (target) distributions, as in dynamic modelling and predictive model selection. Alternative simulation techniques for tracking moving target distributions, known as particle filters, which combine importance sampling, importance resampling and MCMC sampling, tend to suffer from a progressive degeneration as the target sequence evolves. We propose a new technique, based on these same simulation methodologies, which does not suffer from this progressive degeneration. |
| تدمد: | 1467-9868 1369-7412 |
| DOI: | 10.1111/1467-9868.00280 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::0b3451944519ecd8b5b3f28379c35b32 https://doi.org/10.1111/1467-9868.00280 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi...........0b3451944519ecd8b5b3f28379c35b32 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 14679868 13697412 |
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| DOI: | 10.1111/1467-9868.00280 |