By Hay J.L., Pettitt A.N.
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Extra resources for Bayesian analysis of a time series of counts with covariates an application to the control of an infectious disease
E. is physically determined. However, the environment and its component attributes, such as the soil, result from many physical and biological processes Geostatistics for Environmental Scientists/2nd Edition # 2007 John Wiley & Sons, Ltd R. A. Oliver 48 Characterizing Spatial Processes that interact, some in highly non-linear and chaotic ways. The outcome is so complex that the variation appears to be random. This complexity, together with our current, far from complete, understanding of the processes, means that mathematical functions are not adequate to describe any but the simplest components.
6) exceeding the 5% value ðx2p¼0:05; f ¼18 ¼ 28:87Þ, where p signifies the probability and f the degrees of freedom. 1) is close to the 5% value. The reason, as mentioned above, lies largely in having so many data, so that the test is very sensitive. 1 Spatial aspects For spatial data the spatial coordinates must also be checked. The positions of the sampling points can be plotted on a map, referred to in Chapter 1 as a ‘posting’ of the data. Do all the points lie within the region surveyed? If not, why?
Most take account of only systematic or deterministic variation, but not of any error. In these respects, as we shall see, they fall short of what is needed practically. In some ways geostatistical prediction, kriging, is the logical conclusion of these attempts in that it builds on them and overcomes their weaknesses. Nearly all the methods of prediction, including the simpler forms of kriging, can be seen as weighted averages of data. Thus we have the general prediction formula zÃ ðx0 Þ ¼ N X li zðxi Þ; i¼1 Geostatistics for Environmental Scientists/2nd Edition # 2007 John Wiley & Sons, Ltd R.
Bayesian analysis of a time series of counts with covariates an application to the control of an infectious disease by Hay J.L., Pettitt A.N.