Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes

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A Probabilistic Approach to Collaborative Multi-Robot Localization (2000, background): “The tree is grown by recursive ly splitting each node until a certain stopping condition is fulfilled (see [69] for details).”
Adapting the Sample Size in Particle Filters Through KLD-Sampling (2003, methodology): “…a bound on the approximation error, we assume that the true posterior is given by a discrete, piecewise constant distribution such as a discrete density tree or a multidimensional histogram (Koller and Fratkina 1998; Moore, Schneider, and Deng 1997; Omohundro 1991; Thrun, Langford, and Fox 1999).”
Inferring High-Level Behavior from Low-Level Sensors (2003, methodology): “The Monte Carlo version of the Baum-Welch algorithm [19] performs at each iteration both a forward and a backward (in time) particle filtering step.”
Kernel Bayes' rule: Bayesian inference with positive definite kernels (2013, methodology): “Alternatively, nonparametric estimates of conditional density functions can be employed, including kernel density estimation or distribution estimates on a partitioning of the space (Monbet et al., 2008; Thrun et al., 1999).”