Download Bayesian Population Analysis using Win: BUGS. A hierarchical by Marc Kery and Michael Schaub (Auth.) PDF

By Marc Kery and Michael Schaub (Auth.)

ISBN-10: 0123870208

ISBN-13: 9780123870209

Bayesian facts has exploded into biology and its sub-disciplines, similar to ecology, during the last decade. The loose software WinBUGS, and its open-source sister OpenBugs, is at the moment the one versatile and general-purpose software to be had with which the typical ecologist can behavior typical and non-standard Bayesian statistics.

  • Comprehensive and richly commented examples illustrate quite a lot of types which are so much correct to the study of a latest inhabitants ecologist
  • All WinBUGS/OpenBUGS analyses are thoroughly built-in in software program R
  • Includes whole documentation of all R and WinBUGS code required to behavior analyses and exhibits all the mandatory steps from having the information in a textual content dossier out of Excel to examining and processing the output from WinBUGS in R

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Extra info for Bayesian Population Analysis using Win: BUGS. A hierarchical perspective

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7 EXERCISES 1. Detection probability: convince yourself that very few quantities in nature are ever perfectly detectable. Stand at the window for 1 min and make a list of all the bird species that you see. Repeat this once or twice, then compare the lists among times and observers. ), then all the lists would be the same for all observers and it would not matter for how long you watched. Essentially, you would detect all species instantaneously. You may conduct that exercise with a quantity of your choice: for example, the number (or identity) of people in your office hall, and the number of people in your bus.

PðA j BÞ = pðB j AÞpðAÞ pðBÞ This says that the conditional probability of observing A, given that B has happened or is true, p(A|B), is equal to the conditional probability of observing B given A, p(B|A), times the marginal probability of A, p(A), divided by the marginal probability of B, p(B). To better see how Bayes rule works for statistical learning from data, consider the following example which is inspired by a similar example in Pigliucci (2002). Assume that our activity after work consists of bird watching (B) or watching football on TV (F) and that this depends on whether the weather is good (g) or bad (b) on a particular night.

For instance, if we know something for almost certain, we would require large quantities of data to overthrow that prior belief. In contrast, if we do not know anything at all about a system, we might be happy to draw a conclusion based on very little data. 4 FREQUENTIST AND BAYESIAN ANALYSIS OF STATISTICAL MODELS 35 data. In Bayes rule, this weighting of information happens in a formal and mathematically rigorous way. As another illustration of this point, note that in virtually every analysis in ecology we know something about the system analyzed and we always use that information, even in a frequentist framework.

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