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Backward Simulation Methods for Monte Carlo Statistical Inference (Paperback)
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Backward Simulation Methods for Monte Carlo Statistical Inference (Paperback)
Series: Foundations and Trends (R) in Machine Learning
Expected to ship within 10 - 15 working days
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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 that are based on the
idea of processing the data sequentially; first in the forward
direction, and then in the backward direction. Backward Simulation
Methods for Monte Carlo Statistical Inference reviews a branch of
Monte Carlo methods that are based on the forward-backward idea,
and that are referred to as backward simulators. In recent years,
the theory and practice of backward simulation algorithms have
undergone a significant development, 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, this book also clearly shows
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. This
monograph discusses several related backward-simulation-based
methods for state inference as well as learning of static
parameters, both using a frequentistic and a Bayesian approach.
Backward Simulation Methods for Monte Carlo Statistical Inference
is an excellent primer for anyone interested in this active
research area.
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