97,675 research outputs found
Bayesian mixture models: A blood-free dissection of a sheep
No full textThe use of computed tomography (CT) scanning to measure attributes of tissue
composition in animal experiments has grown steadily since the early 1990s. This
technology is used on a range of experiments, such as nutrition trials for live animals,
as well as on carcases after slaughter.
A CT scan returns measurements averaged over a pixel area that represent the
denseness of the tissue. This tissue denseness is related to tissue type, with fat being
generally less dense then muscle and bone being the most dense tissue we study.
However, tissue denseness is not well separated, leading to a large overlap on the
boundaries between types.
Normal mixture models have proved to be an efficient analytical technique for
estimating the proportion of tissue types in individual CT scans, with MCMC output
providing measures of variability that are unavailable in the standard cut-point
modelling approach. These models are then used in conjunction with integration
techniques to estimate the tissue volumes within a carcase.
In this paper we initially model individual scan data using a hierarchical mixture
model, where skewed tissue densities are represented by the addition of two or
more components.The mixture model is then extended to account for some of the
spatial information using a Markov random field represented by a Potts model in
terms of the allocation vector. A scheme for choosing starting values for component
parameters is presented. The paper concludes with the use of the Cavalieri approach
to combine individual scan estimates in order to estimate the carcase volume.No Full Tex
Kurtosis modelling by means of the J-transformation
No full textThe H-family of distributions or H-distributions, introduced by Tukey (1960, 1977), are generated by a single transformation of the standard normal distribution and allow for leptokurtosis represented by the parameter h. Alternatively, Haynes, MacGillivray and Mengersen (1997) generated leptokurtic distributions by applying the K-transformation to the normal distribution. In this study we propose a third transformation - the so-called J-transformation - and derive some properties of this transformation. Moreover, so-called elongation generating functions (EGF’s) are introduced. By means of EGF's we are able to visualize the strength of tail elongation and to construct new transformations. Finally, we compare the three transformations towards their goodness-of-fit in the context of financial return data. --kurtosis,variable transformation,normal transformation,tail elongation.
Estimating detection rates and probabilities
No full textQuantitative Approaches Frith Jarrad, Samantha Low-Choy, Kerrie Mengersen ...
CAB International 2015. Biosecurity Surveillance: Quantitative Approaches (eds
1 Introduction to Biosecurity Surveillance: Quantitative Approaches
Joshua Davis: Author of Spare Parts
No full textCitation: K-State First (2016). Joshua Davis: Author of Spare Parts [Flier]. Manhattan, Kansas: K-State First.Flyer advertising Joshua Davis's author talk at Kansas State University
Using Bayesian networks to model surveillance in complex plant and animal health systems
No full textIn this chapter we consider biosecurity surveillance as part of a complex system comprising many different biological, environmental and human factors and their interactions. Modelling and analysis of surveillance strategies should take into account these complexities, and also facilitate the use and integration of the many types of different information that can provide insight into the system as a whole. After a brief discussion of a range of options, we focus on Bayesian networks for representing such complex systems. We summarize the features of Bayesian networks and describe these in the context of surveillance
Steven Johnson Author Talk Poster
No full textK-State Book NetworkA poster advertising an author talk by Steven Johnson at Kansas State University on September 3, 2014. Steven Johnson's book "The Ghost Map" was the 2014-2015 common book
Unerwünschte Nachbarn. ‚Zigeuner‘ und die Angst vor den Völkern Osteuropas
No full textBogdal K-M. Unerwünschte Nachbarn. ‚Zigeuner‘ und die Angst vor den Völkern Osteuropas. In: von Mengersen O, ed. Sinti und Roma. Eine deutsche Minderheit zwischen Diskriminierung und Emanzipation. Bonn/München; 2015: 87-98
Issues in Designing Hybrid Algorithms
No full texthttps://catalogue.library.auckland.ac.nz/permalink/f/1ilac6l/uoa_alma5122877148000209
The role of intrinsic dimension in high-resolution player tracking data - Insights in basketball
No full textFollowing the introduction of high-resolution player tracking technology,
a new range of statistical analysis has emerged in sports, specifically in basketball.
However, such high dimensional data are often challenging for statistical
inference and decision making. In this article, we employ a state-of-theart
Bayesian mixture model that allows the estimation of heterogeneous s (ID)
within a dataset, and we propose some theoretical enhancements. Informally,
the ID can be seen as an indicator of complexity and dependence of the data
at hand, and it is usually assumed unique. Our method provides the capacity
to reveal valuable insights about the hidden dynamics of sports interactions in
space and time, which helps to translate complex patterns into more coherent
statistics. The application of this technique is illustrated using NBA basketball
players’ tracking data, allowing effective classification and clustering. In
movement data, the analysis identified key stages of offensive actions such
as creating space for passing, preparation/shooting, and following through,
which are relevant for invasion sports. We found that the ID value spikes,
reaching a peak between 4 and 8 seconds, in the offensive part of the court,
after which it declines. In shot charts, we obtained groups of shots that produce
substantially higher and lower successes. Overall, game-winners tend to
have a larger intrinsic dimension, indicative of greater unpredictability and
unique shot placements. Similarly, we found higher ID values in plays when
the score margin is smaller rather than larger. The exploitation of these results
can bring clear strategic advantages in sports games
THE ROLE OF INTRINSIC DIMENSION IN HIGH-RESOLUTION PLAYER TRACKING DATA—INSIGHTS IN BASKETBALL
No full textFollowing the introduction of high-resolution player tracking technology, a new range of statistical analysis has emerged in sports, specifically in basketball. However, such high-dimensional data are often challenging for statistical inference and decision making. In this article we employ a state-of-the-art Bayesian mixture model that allows the estimation of heterogeneous intrinsic dimension (ID) within a dataset, and we propose some theoretical enhancements. Informally, the ID can be seen as an indicator of complexity and dependence of the data at hand, and it is usually assumed unique. Our method provides the capacity to reveal valuable insights about the hidden dynamics of sports interactions in space and time which helps to translate complex patterns into more coherent statistics. The application of this technique is illustrated using NBA basketball players’ tracking data, allowing effective classification and clustering. In movement data the analysis identified key stages of offensive actions, such as creating space for passing, preparation/shooting, and following through which are relevant for invasion sports. We found that the ID value spikes, reaching a peak between four and eight seconds in the offensive part of the court, after which it declines. In shot charts we obtained groups of shots that produce substantially higher and lower successes. Overall, game-winners tend to have a larger intrinsic dimension, indicative of greater unpredictability and unique shot placements. Similarly, we found higher ID values in plays when the score margin is smaller rather than larger. The exploitation of these results can bring clear strategic advantages in sports games
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