Scott Sisson
Scott Sisson
Professor of Statistics & Data Science, University of New South Wales
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TitelCiteras avÅr
Sequential monte carlo without likelihoods
SA Sisson, Y Fan, MM Tanaka
Proceedings of the National Academy of Sciences 104 (6), 1760-1765, 2007
Likelihood-based inference for max-stable processes
SA Padoan, M Ribatet, SA Sisson
Journal of the American Statistical Association 105 (489), 263-277, 2010
A fully probabilistic approach to extreme rainfall modeling
S Coles, LR Pericchi, S Sisson
Journal of Hydrology 273 (1-4), 35-50, 2003
A comparative review of dimension reduction methods in approximate Bayesian computation
MGB Blum, MA Nunes, D Prangle, SA Sisson
Statistical Science 28 (2), 189-208, 2013
In defence of model‐based inference in phylogeography
MA Beaumont, R Nielsen, C Robert, J Hey, O Gaggiotti, L Knowles, ...
Molecular ecology 19 (3), 436-446, 2010
The epidemiological fitness cost of drug resistance in Mycobacterium tuberculosis
F Luciani, SA Sisson, H Jiang, AR Francis, MM Tanaka
Proceedings of the National Academy of Sciences 106 (34), 14711-14715, 2009
Transdimensional Markov chains: A decade of progress and future perspectives
SA Sisson
Journal of the American Statistical Association 100 (471), 1077-1089, 2005
Inference for stereological extremes
P Bortot, SG Coles, SA Sisson
Journal of the American Statistical Association 102 (477), 84-92, 2007
Detection of non-stationarity in precipitation extremes using a max-stable process model
S Westra, SA Sisson
Journal of Hydrology 406 (1-2), 119-128, 2011
Using approximate Bayesian computation to estimate tuberculosis transmission parameters from genotype data
MM Tanaka, AR Francis, F Luciani, SA Sisson
Genetics 173 (3), 1511-1520, 2006
Rapid shifts in dispersal behavior on an expanding range edge
T Lindström, GP Brown, SA Sisson, BL Phillips, R Shine
Proceedings of the National Academy of Sciences 110 (33), 13452-13456, 2013
Bayesian Inference, Monte Carlo Sampling and Operational Risk.
G Peters, S Sisson
Peters GW and Sisson SA (2006)“Bayesian Inference, Monte Carlo Sampling and …, 2006
Likelihood-free MCMC
SA Sisson, Y Fan
Handbook of Markov Chain Monte Carlo, 313-335, 2011
Handbook of approximate Bayesian computation
SA Sisson, Y Fan, M Beaumont
Chapman and Hall/CRC, 2018
Quantifying the dependence between extreme rainfall and storm surge in the coastal zone
F Zheng, S Westra, SA Sisson
Journal of hydrology 505, 172-187, 2013
On sequential Monte Carlo, partial rejection control and approximate Bayesian computation
GW Peters, Y Fan, SA Sisson
Statistics and Computing 22 (6), 1209-1222, 2012
Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments
T Smith, A Sharma, L Marshall, R Mehrotra, S Sisson
Water Resources Research 46 (12), 2010
Likelihood-free Bayesian inference for α-stable models
GW Peters, SA Sisson, Y Fan
Computational Statistics & Data Analysis 56 (11), 3743-3756, 2012
Likelihood-free markov chain monte carlo
SA Sisson, Y Fan
arXiv preprint arXiv:1001.2058, 2010
Adaptive optimal scaling of Metropolis–Hastings algorithms using the Robbins–Monro process
PH Garthwaite, Y Fan, SA Sisson
Communications in Statistics-Theory and Methods 45 (17), 5098-5111, 2016
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