Bayesian Computational Methods in Statistical Signal Processing by Simon John Godsill
The importance of Bayesian signal processing methods have grown over the past decade. A wealth of Bayesian tools are available for solving highly complex inference problems, including particle filters, Markov chain Monte Carlo, and variational Bayes. These methods can be utilized to solve some of the area's major challenges, from state and parameter estimation to decision/control. This book provides full coverage of the background material, including models, inference methods and case studies/examples in an accessible but not overly mathematical style.-
Statistical and Econometric Methods for Transportation Data Analysis
-
Age-Period-Cohort Analysis
-
Measurement Error
-
Clinical Trials in Oncology
- Compositional Data Analysis in Practice
-
Bayesian Analysis of Capture-Recapture Data with Hidden Markov Models
-
Survival Analysis with Interval-Censored Data
-
Statistics for Fission Track Analysis
-
Analysis of Capture-Recapture Data
-
Missing Data Analysis in Practice
-
Power Analysis of Trials with Multilevel Data
-
Bayesian Analysis for Population Ecology
-
Statistical Methods in Epilepsy
-
Applied Directional Statistics
-
An Invariant Approach to Statistical Analysis of Shapes
-
Bayesian Disease Mapping
-
Modern Directional Statistics
-
Statistical Methods in Psychiatry and Related Fields
-
Statistics of Medical Imaging
-
Capture-Recapture Methods for the Social and Medical Sciences
-
Multiple Imputation of Missing Data in Practice
-
Bayesian Modeling of Spatio-Temporal Data with R
-
Clinical Trials in Oncology, Third Edition
-
Correspondence Analysis in Practice
-
Introduction to Computational Biology
-
Model-based Geostatistics for Global Public Health
-
Design of Experiments for Generalized Linear Models
-
Statistics for Biological Networks
-
Parameter Redundancy and Identifiability
-
Model-Based Monitoring and Statistical Control
-
Flexible Imputation of Missing Data, Second Edition
-
The Data Book
-
Mendelian Randomization
-
Statistical Analysis of Questionnaires
-
Design and Analysis of Quality of Life Studies in Clinical Trials
-
Statistical Detection and Surveillance of Geographic Clusters
Simon John Godsill, PH.D., is a professor of statistical signal processing in the Engineering Department at the University of Cambridge, UK. Pete Bunch is a Ph.D. student at the University of Cambridge.
| SKU | Unavailable |
| ISBN 13 | 9781466590212 |
| ISBN 10 | 1466590211 |
| Title | Bayesian Computational Methods in Statistical Signal Processing |
| Author | Simon John Godsill |
| Series | Chapman And Hall Crc Interdisciplinary Statistics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Taylor & Francis Inc |
| Year published | 2021-01-01 |
| Number of pages | 400 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |