This is a paper in the 'Dynamic Social Network Modeling and Analysis', edited by R. Breiger, K. Carley, and P. Pattison. It is the result of the Committee on Human Factors, Board on Behavioral, Cognitive, and Sensory Sciences. National Academy Press: …
The traditional best linear unbiased prediction procedure ('Kriging') is used in this paper for inference, but within a Bayesian framework. See Brown, Le and Zidek (1994) for an alternative Bayesian formulation. Our approach is to exam how posterior …
In this article we develop a random field model for the mean temperature over the region in the northern United States covering eastern Montana through the Dakotas and northern Nebraska up to the Canadian border. The readings are temperatures at the …
This is the rejoinder to the discussion of 'An Approach to Statistical Spatial-Temporal Modeling of Meteorological Fields' (https://doi.org/10.1080/01621459.1994.10476754)
Spatial phenomena is commonly modelled as a realization from a stochastic process. Even when the reality is unique such models can usefully represent the uncertainty the modeler has about the phenomena. This paper is concerned with predicting for …
This article is concerned with predicting for Gaussian random fields in a way that appropriately deals with uncertainty in the covariance function. To this end, we analyze the best linear unbiased prediction procedure within a Bayesian framework. …