Titlebook: Introduction to Bayesian Methods in Ecology and Natural Resources; Edwin J. Green,Andrew O. Finley,William E. Strawde Textbook 2020 Spring

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General Linear Models,s, is not amenable to analysis via “usual” linear model methodology. For instance, the dependent variable may be a count of some phenomenon, e.g., the number of individuals per plot. Since counts are discrete, they clearly fail the usual normality assumption for dependent variables, which among othe

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Spatial Linear Models,tatistical modeling. Key texts in this field include Cressie (.), Cressie and Wikle (.), Chiles and Delfiner (.), Møller and Waagepetersen (.), Schabenberger and Gotway (.), Wackernagel (.), Diggle and Ribeiro (.), and Banerjee et al. (.). The statistical literature acknowledges that spatial (and te

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Introduction,Bayesian inference in the sciences has become remarkably widespread in the wake of the Markov chain Monte Carlo (MCMC) revolution of the 1990s. MCMC methods permit solutions to Bayesian problems which had previously been mathematically intractable.

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Elementary Bayesian Analyses,Before considering more advanced models which might be used in lieu of standard non-Bayesian approaches such as linear regression or Poisson regression, we start with some relatively simple Bayesian models. These will set the stage for the more sophisticated models covered in later chapters.

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978-3-030-60752-4Springer Nature Switzerland AG 2020