0000000000388516
AUTHOR
Juha Heikkinen
Bayesian Smoothing in the Estimation of the Pair Potential Function of Gibbs Point Processes
A flexible Bayesian method is suggested for the pair potential estimation with a high-dimensional parameter space. The method is based on a Bayesian smoothing technique, commonly applied in statistical image analysis. For the calculation of the posterior mode estimator a new Monte Carlo algorithm is developed. The method is illustrated through examples with both real and simulated data, and its extension into truly nonparametric pair potential estimation is discussed.
Fully Bayesian Approach to Image Restoration with an Application in Biogeography
SUMMARY A common method of studying biogeographical ranges is an atlas survey, in which the research area is divided into a square grid and the data consist of the squares where observations occur. Often the observations form only an incomplete map of the true range, and a method is required to decide whether the blank squares indicate true absence or merely a lack of study there. This is essentially an image restoration problem, but it has properties that make the common empirical Bayesian procedures inadequate. Most notably, the observed image is heavily degraded, causing difficulties in the estimation of spatial interaction, and the assessment of reliability of the restoration is emphasi…