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Library | Item Barcode | Call Number | Material Type | Item Category 1 | Status |
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Searching... | 30000010298695 | QA280 B39 2011 | Open Access Book | Book | Searching... |
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Summary
Summary
'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical models, the book covers approximation schemes, both Monte Carlo and deterministic, and introduces switching, multi-object, non-parametric and agent-based models in a variety of application environments. It demonstrates that the basic framework supports the rapid creation of models tailored to specific applications and gives insight into the computational complexity of their implementation. The authors span traditional disciplines such as statistics and engineering and the more recently established areas of machine learning and pattern recognition. Readers with a basic understanding of applied probability, but no experience with time series analysis, are guided from fundamental concepts to the state-of-the-art in research and practice.
Table of Contents
Contributors |
Preface |
1 Inference and estimation in probabilistic time series modelsDavid Barber and A. Taylan Cemgil and Silvia Chiappa |
Part I Monte Carlo: |
2 Adaptive Markov chain Monte Carlo: theory and methodsYves Atchadé and Gersende Fort and Eric Moulines and Pierre Priouret |
3 Auxiliary particle filtering: recent developmentsNick Whiteley and Adam M. Johansen |
4 Monte Carlo probabilistic inference for diffusion processes: a methodological frameworkOmiros Papaspiliopoulos |
Part II Deterministic Approximations: |
5 Two problems with variational expectation maximisation for time series modelsRichard Eric Turner and Maneesh Sahani |
6 Approximate inference for continuous-time Markov processesCédric Archambeau and Manfred Opper |
7 Expectation propagation and generalised EP methods for inference in switching linear dynamical systemsOnno Zoeter and Tom Heskes |
8 Approximate inference in switching linear dynamical systems using Gaussian mixturesDavid Barber |
Part III Change-Point Models: |
9 Analysis of change-point modelsIdris A. Eckley and Paul Fearnhead and Rebecca Killick |
Part IV Multi-Object Models: |
10 Approximate likelihood estimation of static parameters in multi-target modelsSumeetpal S. Singh and Nick Whiteley and Simon J. Godsill |
11 Sequential inference for dynamically evolving groups of objectsSze Kim Pang and Simon J. Godsill and Jack Li and François Septier and Simon Hill |
12 Non-commutative harmonic analysis in multi-object trackingRisi Kondor |
13 Physiological monitoring with factorial switching linear dynamical systemsJohn A. Quinn and Christopher K. I. Williams |
Part V Non-Parametric Models: |
14 Markov chain Monte Carlo algorithms for Gaussian processesMichalis K. Titsias and Magnus Rattray and Neil D. Lawrence |
15 Non-parametric hidden Markov modelsJurgen Van Gael and Zoubin Ghahramani |
16 Bayesian Gaussian process models for multi-sensor time series predictionMichael A. Osborne and Alex Rogers and Stephen J. Roberts and Sarvapali D. Ramchurn and Nick R. Jennings |
Part VI Agent Based Models: |
17 Optimal control theory and the linear Bellman equationHilbert J. Kappen |
18 Expectation-maximisation methods for solving (PO)MDPs and optimal control problemsMarc Toussaint and Amos Storkey and Stefan Harmeling |
Index |