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Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples - Wiley Series in Computational Statistics Liang, Faming (Texas A&M University, USA)
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Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples - Wiley Series in Computational Statistics
Liang, Faming (Texas A&M University, USA)
* Presents the latest developments in Monte Carlo research. * Provides a toolkit for simulating complex systems using MCMC. * Introduces a wide range of algorithms including Gibbs sampler, Metropolis-Hastings and an overview of sequential Monte Carlo algorithms.
378 pages, Illustrations
| Mediji | Grāmatas Hardcover Book (Grāmata ar cieto muguriņu un vāku) |
| Izlaists | 2010. gada 10. augusts |
| ISBN13 | 9780470748268 |
| Izdevēji | John Wiley & Sons Inc |
| Lapas | 384 |
| Izmēri | 233 × 159 × 26 mm · 680 g |
| Valoda | Angļu |
