Hierarchical Bayesian Methods in Ecology
نویسندگان
چکیده
Ecosystems are dynamic in both space and time, hence involve multiple spatial and temporal scales, and are often heterogeneous in both of those dimensions, leading to spatial and temporal clustering. Accommodating this complexity in the context of scientific (statistical) hypothesis testing necessitates more advanced methods than those available within the classical null hypothesis testing paradigm.
منابع مشابه
Bayesian methods for hierarchical models: are ecologists making a Faustian bargain?
It is unquestionably true that hierarchical models represent an order of magnitude increase in the scope and complexity of models for ecological data. The past decade has seen a tremendous expansion of applications of hierarchical models in ecology. The expansion was primarily due to the advent of the Bayesian computational methods. We congratulate the authors for writing a clear summary of hie...
متن کاملWhen mechanism matters: Bayesian forecasting using models of ecological diffusion.
Ecological diffusion is a theory that can be used to understand and forecast spatio-temporal processes such as dispersal, invasion, and the spread of disease. Hierarchical Bayesian modelling provides a framework to make statistical inference and probabilistic forecasts, using mechanistic ecological models. To illustrate, we show how hierarchical Bayesian models of ecological diffusion can be im...
متن کاملPoisson / gamma random field models for spatial statistics
Doubly stochastic Bayesian hierarchical models are introduced to account for uncertainty and spatial variation in the underlying intensity measure for point process models. Inhomogeneous gamma process random fields and, more generally, Markov random fields with infinitely divisible distributions are used to construct positively autocorrelated intensity measures for spatial Poisson point process...
متن کاملBayesian change point estimation in Poisson-based control charts
Precise identification of the time when a process has changed enables process engineers to search for a potential special cause more effectively. In this paper, we develop change point estimation methods for a Poisson process in a Bayesian framework. We apply Bayesian hierarchical models to formulate the change point where there exists a step < /div> change, a linear trend and a known multip...
متن کاملA hierarchical model for spatial capture-recapture data.
Estimating density is a fundamental objective of many animal population studies. Application of methods for estimating population size from ostensibly closed populations is widespread, but ineffective for estimating absolute density because most populations are subject to short-term movements or so-called temporary emigration. This phenomenon invalidates the resulting estimates because the effe...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2010