Inference in Hidden Markov Models (Springer Series in Statistics)

By Olivier Capp'e, Eric Moulines, Tobias Rydén. 2005

407 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ba7598cff4ce3da4be12434451ef891ece61e77a

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Citations in the corpus

A MOSUM procedure for the estimation of multiple random change points (2018, methodology): “For special cases such as Hidden Markov Models, one can make use of the structure of the unobservable process to reconstruct the sequence of states by use of for example, the Viterbi algorithm (confer [8]).”
Uniform time average consistency of Monte Carlo particle filters (2008, background): “the collection [10] for a broad introduction to the theory and applications of Monte Carlo particle ﬁlters, while a detailed overview of theoretical developments can be found in the recent monographs [6, 4]. Despite many advances in recent years, however, certain empirically observed properties of M”
The Markov chain Monte Carlo revolution (2008, unknown): “An important and readable source is [13].”
Identification of jump Markov linear models using particle filters (2014, methodology): “There are also many approaches considering the more general problem with an unknown number of modes K and an unknown state dimension nz, see e.g. [12] and [4], making use of Bayesian nonparametric models and mixed integer programming, respectively.”
Program E-Surge: A Software Application for Fitting Multievent Models (2009, background): “Thus, these models belong to the class of Hidden Markov Models (HMM), see for example MacDonald and Zucchini (2000) and Capp´e et al. (2005).”
Hidden Markov models with t components. Increased persistence and other aspects (2011, methodology): “A more comprehensive and detailed survey on HMMs was reported by Cappé et al. (2007).”
Adaptive sequential Monte Carlo by means of mixture of experts (2011, methodology): “…a tool for solving spe-ciﬁc problems, such as the optimal ﬁltering problem in general state-space models or simulation of rare events (these topics are abundantly covered in Liu 2001; Doucet et al. 2001; Ristic et al. 2004; Cappé et al. 2005; Del Moral et al. 2006, and the references therein).”
Improving bug triage with bug tossing graphs (2009, background): “In contrast, hidden Markov models use all previous states [11].”
Adaptive methods for sequential importance sampling with application to state space models (2008, background): “Another function-free risk criterion to assess the performance of importance sampling estimators is the KLD between the proposal and the target distributions; see (Cappé et al., 2005, Chapter 7).”
Large sample asymptotics for the ensemble Kalman filter (2009, background): “Indeed, there exists another class of Monte Carlo based approximations to the Bayesian lter, which have been extensively studied both practically and theoretically [4, 6, 7], and which could be applied to a much broader class of nonlinear models or even more general hidden Markov models.”
Adaptive importance sampling in general mixture classes (2007, methodology): “The entropy of the IS weights is frequently used as a criterion for assessing the quality of an IS sample—together with the so-called Effective Sample Size (ESS) (Chen and Liu, 1996, Doucet et al., 2001, Cappé et al., 2005).”
Inference in finite state space non parametric Hidden Markov Models and applications (2015, methodology): “Efﬁcient algorithms allow to compute the likelihood and to build practical inference methods for both mixture and HMMs, see e.g. Cappé et al. (2005) for a recent state of the art in HMMs.”