Inference in hidden Markov models

By Olivier Capp'e, Eric Moulines, Tobias Rydén. Springer Series in Statistics, 2010

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Hidden semi-Markov models (2010, neutral): “[29].”
Truncated Importance Sampling (2008, background): “Many of these techniques are less broadly applicable than likelihood reconstruction (Hürzeler and Künsch, 2001; Cappé et al., 2005; Beskos et al., 2006), and cannot readily be applied to the general multivariate nonlinear diffusion framework of Section 7.”
Some Parameter Estimation Issues in Functional-Structural Plant Modelling (2011, background): “[6]): model equations can be interpreted as a bidimensional stochastic process, which describes the evolution of system variables.”
Fast Direct Methods for Gaussian Processes (2014, background): “to the state-space interpretation of Gaussian processes), similar to hidden Markov models [12], [38], [51].”
An Overview of Existing Methods and Recent Advances in Sequential Monte Carlo (2007, methodology): “Such approximations have been used with reasonable successes either using Monte Carlo versions of the EM algorithm [7] or stochastic gradient procedures [89].”
Count Time Series Models (2012, methodology): “More generally, state space models for count time series are discussed in West and Harrison (1997), Durbin and Koopman (2001) and Cappé et al. (2005) among others.”