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Book Categories |
| Foreword | ||
| Acknowledgments | ||
| Contributors | ||
| I | Introduction | 1 |
| 1 | An Introduction to Sequential Monte Carlo Methods | 3 |
| II | Theoretical Issues | 15 |
| 2 | Particle Filters - A Theoretical Perspective | 17 |
| 3 | Interacting Particle Filtering With Discrete Observations | 43 |
| III | Strategies for Improving Sequential Monte Carlo Methods | 77 |
| 4 | Sequential Monte Carlo Methods for Optimal Filtering | 79 |
| 5 | Deterministic and Stochastic Particle Filters in State-Space Models | 97 |
| 6 | RESAMPLE-MOVE Filtering with Cross-Model Jumps | 117 |
| 7 | Improvement Strategies for Monte Carlo Particle Filters | 139 |
| 8 | Approximating and Maximising the Likelihood for a General State-Space Model | 159 |
| 9 | Monte Carlo Smoothing and Self-Organising State-Space Model | 177 |
| 10 | Combined Parameter and State Estimation in Simulation-Based Filtering | 197 |
| 11 | A Theoretical Framework for Sequential Importance Sampling with Resampling | 225 |
| 12 | Improving Regularised Particle Filters | 247 |
| 13 | Auxiliary Variable Based Particle Filters | 273 |
| 14 | Improved Particle Filters and Smoothing | 295 |
| IV | Applications | 319 |
| 15 | Posterior Cramer-Rao Bounds for Sequential Estimation | 321 |
| 16 | Statistical Models of Visual Shape and Motion | 339 |
| 17 | Sequential Monte Carlo Methods for Neural Networks | 359 |
| 18 | Sequential Estimation of Signals under Model Uncertainty | 381 |
| 19 | Particle Filters for Mobile Robot Localization | 401 |
| 20 | Self-Organizing Time Series Model | 429 |
| 21 | Sampling in Factored Dynamic Systems | 445 |
| 22 | In-Situ Ellipsometry Solutions Using Sequential Monte Carlo | 465 |
| 23 | Manoeuvring Target Tracking Using a Multiple-Model Bootstrap Filter | 479 |
| 24 | Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks | 499 |
| 25 | Particles and Mixtures for Tracking and Guidance | 517 |
| 26 | Monte Carlo Techniques for Automated Target Recognition | 533 |
| Bibliography | 553 | |
| Index | 577 |
| Price | Condition | Delivery | Seller | Action |
| $109.00 | Digital |
| WonderClub |
Matt Franks
reviewed Sequential Monte Carlo Methods in Practice on February 13, 2018
Sequential Monte Carlo Methods in Practice
If you've already read some of the cryptography canon (i.e. Schneier), the first three quarters or so of Modern Cryptanalysis, which, conceived as an introduction to cryptanalysis for the motivated but ultimately completely uninformed layperson as the book is, are spent explaining what cryptography is and looks like (including a whole chapter on factoring and discrete logarithms), won't be new to you. The main broad cryptanalytic techniques covered'linear and differential cryptanalysis'also won't hold any significant surprises, though Swenson obviously covers the material much more thoroughly and includes actual implementations (in moderately dubious Python) and exercises.
The main thing I got out of this book that I didn't get from any of the other, older books on crypto I've read was actually his description of elliptic curve cryptography (and ~ factorisation), which I'd obviously seen mentioned but not elaborated on before. If you haven't yet read Applied or Practical Cryptography or genuinely want to be able to perform linear or differential cryptanalysis yourself, you'll probably find this book much more useful, though it's a fun read either way.
Because of its much shorter length (about 230 pages) compared to most other books worth reading on the subject, parts of it seem a lot more daunting than they are, but the whole thing is pretty well written and easy to follow.
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