{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T18:59:45Z","timestamp":1774897185570,"version":"3.50.1"},"reference-count":148,"publisher":"Emerald","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2013,8,30]]},"abstract":"<jats:p>Monte Carlo methods, in particular those based on Markov chains and on interacting particle systems, are by now tools that are routinely used in machine learning. These methods have had a profound impact on statistical inference in a wide range of application areas where probabilistic models are used. Moreover, there are many algorithms in machine learning which are based on the idea of processing the data sequentially, first in the forward direction and then in the backward direction. In this tutorial, we will review a branch of Monte Carlo methods based on the forward\u2013backward idea, referred to as backward simulators. These methods are useful for learning and inference in probabilistic models containing latent stochastic processes. The theory and practice of backward simulation algorithms have undergone a significant development in recent years and the algorithms keep finding new applications. The foundation for these methods is sequential Monte Carlo (SMC). SMC-based backward simulators are capable of addressing smoothing problems in sequential latent variable models, such as general, nonlinear\/non-Gaussian state-space models (SSMs). However, we will also clearly show that the underlying backward simulation idea is by no means restricted to SSMs. Furthermore, backward simulation plays an important role in recent developments of Markov chain Monte Carlo (MCMC) methods. Particle MCMC is a systematic way of using SMC within MCMC. In this framework, backward simulation gives us a way to significantly improve the performance of the samplers. We review and discuss several related backward-simulation-based methods for state inference as well as learning of static parameters, both using a frequentistic and a Bayesian approach.<\/jats:p>","DOI":"10.1561\/2200000045","type":"journal-article","created":{"date-parts":[[2013,8,30]],"date-time":"2013-08-30T08:41:45Z","timestamp":1377852105000},"page":"1-143","source":"Crossref","is-referenced-by-count":90,"title":["Backward Simulation Methods for Monte Carlo Statistical Inference"],"prefix":"10.1561","volume":"6","author":[{"given":"Fredrik","family":"Lindsten","sequence":"first","affiliation":[{"name":"Link\u00f6ping University Division of Automatic Control, , Link\u00f6ping, 581 83,","place":["Sweden"]}]},{"given":"Thomas B.","family":"Sch\u00f6n","sequence":"additional","affiliation":[{"name":"Link\u00f6ping University Division of Automatic Control, , Link\u00f6ping, 581 83,","place":["Sweden"]}]}],"member":"140","published-online":{"date-parts":[[2013,8,30]]},"reference":[{"key":"2026033014015916100_ref001","doi-asserted-by":"crossref","DOI":"10.1007\/BFb0099420","volume-title":"\u00c9cole d\u2019\u00c9t\u00c9 de Probabilit\u00c9s de Saint-Flour XIII\u20131983","author":"Aldous","year":"1985"},{"issue":"1","key":"2026033014015916100_ref002","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1020281327116","article-title":"An introduction to MCMC for machine learning","volume":"50","author":"Andrieu","year":"2003","journal-title":"Machine Learning"},{"issue":"3","key":"2026033014015916100_ref003","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1111\/j.1467-9868.2009.00736.x","article-title":"Particle Markov chain Monte Carlo methods","volume":"72","author":"Andrieu","year":"2010","journal-title":"Journal of the Royal Statistical Society: Series B"},{"key":"2026033014015916100_ref004","volume-title":"Proceedings of the 2000 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1111.5421, November2011."},{"key":"2026033014015916100_ref008","unstructured":"Andrieu\n              C.\n            \n            \n              Vihola\n              M.\n            \n          , Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms, arXiv.org, 1210.1484, October2012."},{"issue":"6","key":"2026033014015916100_ref009","doi-asserted-by":"crossref","first-page":"1152","DOI":"10.1214\/aos\/1176342871","article-title":"Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems","volume":"2","author":"Antoniak","year":"1974","journal-title":"The Annals of Statistics"},{"issue":"2","key":"2026033014015916100_ref010","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1109\/78.978374","article-title":"A tutorial on particle filters for online nonlinear\/non-Gaussian Bayesian tracking","volume":"50","author":"Arulampalam","year":"2002","journal-title":"IEEE Transactions on Signal 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K.\n            \n            \n              Kohn\n              R.\n            \n          , Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator, arXiv.org, 1210.1871, October2012."},{"issue":"2","key":"2026033014015916100_ref044","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/0370-2693(87)91197-X","article-title":"Hybrid Monte Carlo","volume":"195","author":"Duane","year":"1987","journal-title":"Physics Letters B"},{"key":"2026033014015916100_ref045","doi-asserted-by":"crossref","unstructured":"Dubarry\n              C.\n            \n            \n              Le Corff\n              S. 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