An Introduction to Sequential Monte Carlo by Nicolas Chopin
This book provides a general introduction to Sequential Monte Carlo (SMC) methods, also known as particle filters. Bayesian inference or rare-event problems), are also discussed. The book may be used either as a graduate text on Sequential Monte Carlo methods and state-space modeling, or as a general reference work on the area.-
The Elements of Statistical Learning
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Dragons of Winter Night
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Functional Data Analysis
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Time Series: Theory and Methods
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Modeling Discrete Time-to-Event Data
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Targeted Learning
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Models for Discrete Longitudinal Data
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Regression Modeling Strategies
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Targeted Learning in Data Science
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Modern Multidimensional Scaling
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Correlation Theory of Stationary and Related Random Functions
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Mathematical Statistics
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Breakthroughs in Statistics
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Statistical Models Based on Counting Processes
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Hidden Markov Processes and Adaptive Filtering
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Design of Observational Studies
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Chaos: A Statistical Perspective
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A Comparison of the Bayesian and Frequentist Approaches to Estimation
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Smoothing Spline ANOVA Models
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Analysis of Neural Data
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Finite Mixture and Markov Switching Models
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The Gini Methodology
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Ten Projects in Applied Statistics
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Growth Curve Models and Statistical Diagnostics
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Statistical Methods in Software Engineering
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Markov Bases in Algebraic Statistics
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Data
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A Course on Point Processes
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Shrinkage Estimation
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Theory of Statistics
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Bayesian and Frequentist Regression Methods
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A Statistical Model
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Prediction Theory for Finite Populations
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Tools for Statistical Inference
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Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series
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ARMA Model Identification
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Statistical Design and Analysis for Intercropping Experiments
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Approximate Distributions of Order Statistics
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Robust Asymptotic Statistics
Omiros Papaspiliopoulos (PhD, Lancaster University, 2003) is an ICREA Research Professor and Director of the Data Science Center at Barcelona Graduate School of Economics. Previous positions include Full Professor at Universitat Pompeu Fabra, Assistant Professor at Warwick University and Research Associate at Lancaster and Oxford University.
He is currently co-editor of Biometrika, and has been an Associate Editor for the Journal of the Royal Statistical Society Series B, Biometrika, Journal of Uncertainty Quantification (SIAM) and Statistics and Computing. He has delivered more than 100 invited talks, and has given courses at ENSAE in Paris, the Berlin Mathematical School, the Department of Mathematics at the University of Copenhagen, and the Engineering Department at Osaka University. In 2010 he was awarded the Royal Statistical Society’s Guy Medal in Bronze.
His research interests include computational statistics, applied mathematics and machine learning.
Nicolas Chopin (PhD, Université Pierre et Marie Curie, Paris, 2003) has been a Professor of Statistics at ENSAE, Paris, since 2006. He was previously a lecturer at Bristol University (UK).
He is a current or former associate editor for Annals of Statistics, Biometrika, Journal of the Royal Statistical Society, Statistics and Computing, and Statistical Methods & Applications. He has served as a member (2013-14) and secretary (2015-16) of the research section committee of the Royal Statistical Society. He received a Savage Award for his doctoral dissertation in 2002.
His research interests include computational statistics, Bayesian inference, and machine learning..
| SKU | Unavailable |
| ISBN 13 | 9783030478445 |
| ISBN 10 | 3030478440 |
| Title | An Introduction to Sequential Monte Carlo |
| Author | Nicolas Chopin |
| Series | Springer Series In Statistics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer Nature Switzerland AG |
| Year published | 2020-10-02 |
| Number of pages | 378 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |