Adrian Baddeley | |
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Born | Melbourne, Australia | May 25, 1955
Education | Australian National University University of Cambridge |
Awards |
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Scientific career | |
Fields | Statistics |
Institutions | University of Bath University of Western Australia CSIRO Curtin University |
Doctoral advisor | David George Kendall |
Website | oasisapps |
Adrian John Baddeley (born May 25, 1955) [1] is a statistical scientist working in the fields of spatial statistics, [2] statistical computing, stereology [3] and stochastic geometry.
Baddeley was born in Melbourne, Australia and educated at Eltham High School there, and studied mathematics and statistics at the Australian National University (honours supervisor: Roger Miles) and the University of Cambridge (PhD supervisor: David George Kendall). He was elected a Junior Research Fellow at Trinity College, Cambridge in the second year of his PhD. Subsequently, he worked for the University of Bath (1982–85), the CSIRO Division of Mathematics and Statistics, Sydney (1985–88), the Centrum Wiskunde & Informatica, Amsterdam, the Netherlands (1988–94), the University of Western Australia (where he was Professor of Statistics from 1994 to 2010), CSIRO Division of Mathematics, Informatics and Statistics, Perth (2010-2012), and the Centre for Exploration Targeting at the University of Western Australia (2013-2014). He is now Professor of Computational Statistics at Curtin University.
Classical methods of stereology were limited by the requirement that the cutting plane be randomly oriented. Baddeley developed an alternative technique [4] in which the cutting plane is "vertical" (parallel to a fixed axis, or perpendicular to a fixed surface) making it possible to apply quantitative microscopy to cylindrical core samples, samples of flat materials, and longitudinal sections.
Baddeley is a leading advocate of statistical ideas in stereology. With Cruz-Orive he demonstrated the role of the Horvitz-Thompson weighting principle and the Rao-Blackwell theorem in stereological sampling. [3]
Baddeley is one of the world leading specialists in point pattern analysis, a connection of stochastics and geometry applied to the analysis of (mainly) 2D point distributions in euclidean space. He has developed statistical methodologies for analyzing the structure of spatial patterns of points, including methods based on survival analysis, [5] nonparametrics, [6] [7] new point process models, [8] [9] model-fitting principles (i.e. 'regression analysis' for point patterns) and algorithms [10] [11] [12] and open-source software. [13]
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In probability and statistics, a nearest neighbor function, nearest neighbor distance distribution, nearest-neighbor distribution function or nearest neighbor distribution is a mathematical function that is defined in relation to mathematical objects known as point processes, which are often used as mathematical models of physical phenomena representable as randomly positioned points in time, space or both. More specifically, nearest neighbor functions are defined with respect to some point in the point process as being the probability distribution of the distance from this point to its nearest neighboring point in the same point process, hence they are used to describe the probability of another point existing within some distance of a point. A nearest neighbor function can be contrasted with a spherical contact distribution function, which is not defined in reference to some initial point but rather as the probability distribution of the radius of a sphere when it first encounters or makes contact with a point of a point process.
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