Download Bayesian Population Analysis using WinBUGS. A hierarchical by Marc Kery, Michael Schaub PDF

By Marc Kery, Michael Schaub

Bayesian data has exploded into biology and its sub-disciplines, resembling ecology, during the last decade. The unfastened software WinBUGS, and its open-source sister OpenBugs, is at present the one versatile and general-purpose application on hand with which the common ecologist can behavior ordinary and non-standard Bayesian statistics.

  • Comprehensive and richly commented examples illustrate quite a lot of versions which are so much proper to the learn of a latest inhabitants ecologist
  • All WinBUGS/OpenBUGS analyses are thoroughly built-in in software program R
  • Includes entire documentation of all R and WinBUGS code required to behavior analyses and exhibits all the required steps from having the knowledge in a textual content dossier out of Excel to studying and processing the output from WinBUGS in R

Show description

Read or Download Bayesian Population Analysis using WinBUGS. A hierarchical perspective PDF

Best mathematical & statistical books

Maths & Stats Handbook of Computational Statistics

The instruction manual of Computational records - strategies and techniques ist divided into four elements. It starts with an summary of the sector of Computational records, the way it emerged as a seperate self-discipline, the way it constructed alongside the advance of not easy- and software program, together with a discussionof present lively study.

SPSS 16.0 Brief Guide

The SPSS sixteen. zero short consultant presents a suite of tutorials to acquaint you with the parts of the SPSS method. issues contain studying facts, utilizing the knowledge Editor, studying precis information for person variables, operating with output, growing and enhancing charts, operating with syntax, editing info values, sorting and choosing info, and acting extra statistical strategies.

Computer Models in Environmental Planning

The aim at the back of machine types in Environmental making plans is to supply a pragmatic and utilized consultant to using those types in environmental making plans and environmental effect research. types bearing on water caliber, air caliber, stormwater runoff, land capabil­ ity evaluationfland details platforms, and dangerous waste dis­ posal are reviewed and critiqued.

Statistische Datenanalyse mit SPSS Für Windows: Eine anwendungsorientierte Einführung in das Basissystem und das Modul Exakte Tests

Die 6. Auflage basiert auf Programmversion 15. Die Autoren demonstrieren mit möglichst wenig Mathematik, detailliert und anschaulich anhand von Beispielen aus der Praxis die statistischen Methoden und deren Anwendungen. Der Anfänger findet für das Selbststudium einen sehr leichten Einstieg in das Programmsystem, für den erfahrenen SPSS-Anwender (auch früherer Versionen) ist das Buch ein hervorragendes Nachschlagewerk.

Additional resources for Bayesian Population Analysis using WinBUGS. A hierarchical perspective

Example text

Explicit hierarchical models have random variables or parameters with an explicit ecological interpretation, while implicit hierarchical models do not. As an example of an implicit hierarchical model, the Poisson GLMMs in Chapter 4 have a quantity called the expected count (λ). This is not a real ecological parameter because it is the product of population size and detection probability. In contrast, in the hierarchical models in Chapter 6, N is the sum of the latent indicator variables z and corresponds exactly to the local population size.

2002), but again, its standard version computed by WinBUGS appears to be problematic for hierarchical models—and most models that ecologists nowadays want to fit have more than one random component and therefore are mixed, or hierarchical, models (see Chapter 4). The DIC can be computed for such hierarchical models (see Millar, 2009, which includes R code), but the required computations are involved and computationally very demanding. Hence, in spite of long-standing criticisms of stepwise model selection and model selection by significance tests, one may effectively be back at one of those.

There are broadly two different objectives of modeling, and they may lead to two different modes of building a model: explanation and prediction. Explanation means understanding and will typically require simpler models than prediction (Caswell, 1988). The explanatory mode of modeling focuses on the actual model structure. It is hoped that the kind of parameters and their values have some relevance for how nature generated the observed output. The focus is more on the parameters θ. In contrast, prediction focuses on the system output, the response y, and thus aims at predicting the response as well as possible either within the sample studied or for the entire statistical population that is represented by the sample.

Download PDF sample

Rated 4.78 of 5 – based on 9 votes