Bayesian Inference
With Ecological Applications
- Publisher's listprice EUR 58.95
-
24 449 Ft (23 285 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
- Discount 20% (cc. 4 890 Ft off)
- Discounted price 19 559 Ft (18 628 Ft + 5% VAT)
- Discount is valid until: 31 December 2025
Subcribe now and take benefit of a favourable price.
Subscribe
24 449 Ft
Availability
printed on demand
Why don't you give exact delivery time?
Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher Elsevier Science
- Date of Publication 7 August 2009
- ISBN 9780123748546
- Binding Hardback
- No. of pages354 pages
- Size 234x190 mm
- Weight 720 g
- Language English 0
Categories
Long description:
This text is written to provide a mathematically sound but accessible and engaging introduction to Bayesian inference specifically for environmental scientists, ecologists and wildlife biologists. It emphasizes the power and usefulness of Bayesian methods in an ecological context.
The advent of fast personal computers and easily available software hasï¿1⁄2simplified the use ofï¿1⁄2Bayesian and hierarchicalï¿1⁄2models . One obstacle remains for ecologists and wildlife biologists, namely the near absence of Bayesian texts written specifically for them. The book includes many relevant examples, is supported by software and examples on a companion website and will become an essential grounding in this approachï¿1⁄2forï¿1⁄2students and research ecologists.
MoreTable of Contents:
Chapter 1. Bayesian InferenceChapter 2. ProbabilityChapter 3. Statistical InferenceChapter 4. Posterior CalculationsChapter 5. Bayesian PredictionChapter 6. PriorsChapter 7. Multimodel InferenceChapter 8. Hidden Data ModelsChapter 9. Closed-Population Mark-Recapture ModelsChapter 10. Latent MultinomialsChapter 11. Open Population ModelsChapter 12. Individual FitnessChapter 13. Autoregressive Smoothing
More