Browsing Department of Statistics (MU) by Subject "Bayesian statistical decision theory"
Now showing items 18 of 8

Accounting for Uncertainty in Ecological Analysis: The Strengths and Limitations of Hierarchical Statistical Modeling
(Ecological Society of America, 200904)Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. ... 
A Bayesian Approach to Estimating the Long Memory Parameter
(Bayesian Analysis, 2009)We develop a Bayesian procedure for analyzing stationary longrange dependent processes. Specifically, we consider the fractional exponential model (FEXP) to estimate the memory parameter of a stationary longmemory Gaussian ... 
Gene ExpressionBased Glioma Classification Using Hierarchical Bayesian Vector Machines
(Indian Statistical Institute, 2007)This paper considers several Bayesian classification methods for the analysis of the glioma cancer with microarray data based on reproducing kernel Hilbert space under the multiclass setup. We consider the multinomial ... 
Hierarchical Bayesian Approach to Boundary Value Problems with Stochastic Boundary Conditions
(American Meteorological Society, 2003)Boundary value problems are ubiquitous in the atmospheric and ocean sciences. Typical settings include bounded, partially bounded, global and limited area domains, discretized for applications of numerical models of the ... 
Hierarchical Bayesian Models for Predicting The Spread of Ecological Processes
(Ecological Society of America, 2003)There is increasing interest in predicting ecological processes. Methods to accomplish such predictions must account for uncertainties in observation, sampling, models, and parameters. Statistical methods for spatiotemporal ... 
A KernelBased Spectral Model for NonGaussian SpatioTemporal Processes
(Statistical Modelling, 2002)Spatiotemporal processes can often be written as hierarchical statespace processes. In situations with complicated dynamics such as wave propagation, it is difficult to parameterize state transition functions for ... 
Predicting the Spatial Distribution of Ground Flora on Large Domains Using a Hierarchical Bayesian Model
(Landscape Ecology, 2003)Accomodation of important sources of uncertainty in ecological models is essential to realistically predicting ecological processes. The purpose of this project is to develop a robust methodology for modeling natural ... 
SpatioTemporal Hierarchical Bayesian Modeling: Tropical Ocean Surface Winds
(American Statistical Association, 2001)Spatiotemporal processes are ubiquitous in the environmental and physical sciences. This is certainly true of atmospheric and oceanic processes, which typically exhibit many different scales of spatial and temporal ...