World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
31
Citations
6052
World Ranking
13431
National Ranking
400

Overview

Scott A. Sisson is affiliated with the University of New South Wales in Australia. Their research primarily focuses on the field of computer science, with a significant emphasis on artificial intelligence and statistics.

The main fields of study for Scott A. Sisson include:

  • Computer Science

Within these fields, the subfields covered are:

  • Artificial Intelligence
  • Statistics and Probability
  • Computer Vision and Pattern Recognition
  • Orthopedics and Sports Medicine
  • Public Health, Environmental and Occupational Health

Their research topics have involved the following areas:

  • Bayesian Methods and Mixture Models
  • Statistical Methods and Inference
  • Markov Chains and Monte Carlo Methods
  • Statistical Methods and Bayesian Inference
  • Bayesian Modeling and Causal Inference
  • Financial Risk and Volatility Modeling
  • Gaussian Processes and Bayesian Inference

Their recent papers cover a variety of applied topics and were published in notable venues. These include:

  • "Ensemble of ARIMA: combining parametric and bootstrapping technique for traffic flow prediction," 2020, Transportmetrica A Transport Science
  • "Tools for enhancing the application of self-organizing maps in water resources research and engineering," 2020, Advances in Water Resources
  • "Machine Learning in Polymer Research," 2025, Advanced Materials
  • "Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data," 2020, Ecology Letters
  • "Predicting seagrass decline due to cumulative stressors," 2020, Environmental Modelling & Software

Scott A. Sisson frequently collaborates with other researchers. Their most frequent co-authors include:

  • Boris Béranger
  • Xuhui Fan
  • David J. Nott
  • Kerrie Mengersen
  • Christopher Drovandi

They have published extensively across several venues, with multiple publications in:

  • arXiv (Cornell University)
  • Statistics and Computing
  • Drug and Alcohol Review
  • Journal of Computational and Graphical Statistics
  • Extremes

Best Publications

  • Sequential Monte Carlo without likelihoods

    S. A. Sisson;Y. Fan;Mark M. Tanaka

  • Likelihood-Based Inference for Max-Stable Processes

    Simone Padoan;Mathieu Ribatet;Scott Sisson

  • A comparative review of dimension reduction methods in approximate Bayesian computation

    Michael Gb Blum;Matthew A. Nunes;Dennis Prangle;Scott A. Sisson

  • Handbook of Approximate Bayesian Computation

    Scott A. Sisson;Yanan Fan;Mark Beaumont

  • A fully probabilistic approach to extreme rainfall modeling

    Stuart Coles;Luis Raúl Pericchi;Scott Sisson

  • In defence of model-based inference in phylogeography.

    Mark A. Beaumont;Rasmus Nielsen;Christian Robert;Jody Hey

  • Modeling dependence between extreme rainfall and storm surge to estimate coastal flooding risk

    Feifei Zheng;Seth Westra;Michael Leonard;Scott A. Sisson

  • Transdimensional markov chains : A decade of progress and future perspectives

    Scott A Sisson

  • Detection of non-stationarity in precipitation extremes using a max-stable process model

    Seth Westra;Scott A. Sisson

  • Using Approximate Bayesian Computation to Estimate Tuberculosis Transmission Parameters From Genotype Data

    Mark M Tanaka;Andrew R Francis;Fabio Luciani;Scott Sisson

  • Inference for stereological extremes

    P Bortot;S. G Coles;S. A Sisson

  • Bayesian Inference, Monte Carlo Sampling and Operational Risk

    G. W. Peters;S. A. Sisson

  • Likelihood-free Markov chain Monte Carlo

    S A Sisson;Y Fan

  • Ensemble of ARIMA: combining parametric and bootstrapping technique for traffic flow prediction

    Siroos Shahriari;Milad Ghasri;S. A. Sisson;Taha Rashidi

  • Development of a formal likelihood function for improved Bayesian inference of ephemeral catchments

    Tyler Smith;Tyler Smith;Ashish Sharma;Lucy Marshall;Raj Mehrotra

  • Likelihood-Free MCMC

    Scott A Sisson;Yanan Fan

  • Likelihood-free Bayesian inference for α-stable models

    G. W. Peters;S. A. Sisson;Y. Fan

  • On sequential Monte Carlo, partial rejection control and approximate Bayesian computation

    Gareth W. Peters;Gareth W. Peters;Yanan Fan;Scott A. Sisson

  • Adaptive optimal scaling of Metropolis–Hastings algorithms using the Robbins–Monro process

    P. H. Garthwaite;Y. Fan;S. A. Sisson

  • Bayesian calibration and uncertainty analysis of hydrological models: A comparison of adaptive Metropolis and sequential Monte Carlo samplers

    Erwin Jeremiah;Scott Sisson;Lucy Marshall;Rajeshwar Mehrotra

  • Diagnostic tools for approximate Bayesian computation using the coverage property

    Dennis Prangle;Michael Gb Blum;G. Popovic;Scott Sisson

  • Approximate Bayesian computation via regression density estimation

    Yanan Fan;David J. Nott;Scott A. Sisson

  • Approximate Bayesian Computation and Bayes’ Linear Analysis: Toward High-Dimensional ABC

    D. J. Nott;Y. Fan;L. Marshall;S. A. Sisson

  • Statistical Inference and Simulation for Spatial Point Processes

    Scott A. Sisson

Frequent Co-Authors

David J. Nott
David J. Nott National University of Singapore
Ashish Sharma
Ashish Sharma University of New South Wales
Mark A. Beaumont
Mark A. Beaumont University of Bristol
Seth Westra
Seth Westra University of Adelaide
Andrew J. Pitman
Andrew J. Pitman University of New South Wales
Stuart J. Khan
Stuart J. Khan University of Sydney
Rasmus Nielsen
Rasmus Nielsen University of California, Berkeley
David J. Balding
David J. Balding University of Melbourne
Laurent Excoffier
Laurent Excoffier University of Bern
Ziheng Yang
Ziheng Yang University College London

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

For students interested in Computer Science but looking for flexible learning options, exploring an online associate degree can be an excellent start. These programs allow you to build foundational knowledge without committing to a four-year program and are suitable for career changers or those balancing work and study.

If affordability is a primary concern, consider applying to cheap online colleges. These institutions offer recognized programs at reduced tuition rates while still providing quality education.

Admission can be another hurdle for some learners. Fortunately, there are online schools that accept low gpa. These schools give more students a second chance to pursue a technology degree and launch a lucrative career.

Beyond computer science, other in-demand fields like environmental science also offer lucrative job prospects. You can learn more about high-paying jobs with environmental science degree programs, which can also be earned online.

Best Scientists Citing Scott A. Sisson

Trending Scientists

Recently Published Articles