Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Scott A. Sisson"'
Publikováno v:
Quantitative Finance. 22:1665-1691
Motivated by the need for effectively summarising, modelling, and forecasting the distributional characteristics of intra-daily returns, as well as the recent work on forecasting histogram-valued time-series in the area of symbolic data analysis, we
Publikováno v:
Journal of Computational and Graphical Statistics. 31:50-63
Likelihood-free methods are an established approach for performing approximate Bayesian inference for models with intractable likelihood functions. However, they can be computationally demanding. B...
Publikováno v:
Bayesian Analysis. 17
Many applications in Bayesian statistics are extremely computationally intensive. However, they are often inherently parallel, making them prime targets for modern massively parallel processors. Multi-core and distributed computing is widely applied
Publikováno v:
Ecography. 2022
Autor:
Nicola Man, Scott A. Sisson, Rebecca McKetin, Agata Chrzanowska, Raimondo Bruno, Paul M. Dietze, Olivia Price, Louisa Degenhardt, Daisy Gibbs, Caroline Salom, Amy Peacock
Publikováno v:
Drug and alcohol reviewReferences. 41(5)
To describe trends in methamphetamine use, markets and harms in Australia from 2003 to 2019.Data comprised patterns of use and price from sentinel samples of people who inject drugs and who use MDMA/other illicit stimulants and population-level amphe
Publikováno v:
Extremes. 24:653-685
Max-stable processes are a popular tool for the study of environmental extremes, and the extremal skew-t process is a general model that allows for a flexible extremal dependence structure. For inference on max-stable processes with high-dimensional
Publikováno v:
Statistics and Computing. 30:1459-1477
Symbolic data analysis has been proposed as a technique for summarising large and complex datasets into a much smaller and tractable number of distributions—such as random rectangles or histograms—each describing a portion of the larger dataset.
Publikováno v:
Statistics and Computing. 30:1057-1073
Likelihood-free methods such as approximate Bayesian computation (ABC) have extended the reach of statistical inference to problems with computationally intractable likelihoods. Such approaches perform well for small-to-moderate dimensional problems,
Publikováno v:
Transportmetrica A: Transport Science. 16:1552-1573
There are numerous studies on traffic volume prediction, using either non-parametric or parametric methods. The main shortcoming of parametric methods is low prediction accuracy. Non-parametric met...
Modeling total predation to avoid perverse outcomes from cat control in a data-poor island ecosystem
Autor:
Michaela Plein, Katherine R. O'Brien, Matthew H. Holden, Matthew P. Adams, Christopher M. Baker, Nigel G. Bean, Scott A. Sisson, Michael Bode, Kerrie L. Mengersen, Eve McDonald‐Madden
Publikováno v:
Conservation biology : the journal of the Society for Conservation Biology.
Data hungry, complex ecosystem models are often used to predict the consequences of threatened species management, including perverse outcomes. Unfortunately, this approach is impractical in many systems, which have insufficient data to parameterize