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ACEMS Workshop: Advances and challenges in Monte Carlo methods

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The University of Queensland

Emmanuel College

Riverview Room

Saint Lucia, QLD 4072

Australia

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Monte Carlo methods permeate much of contemporary science, data science, and machine learning. This workshop brings together practitioners and theorists in Monte Carlo simulation with the aim to highlight new theoretical developments and focus on new challenges ahead.

Keynote speaker:

Sandeep Juneja, Tata Institute of Fundamental Research: Sample complexity of partition identification using multi-armed bandits with applications to nested Monte Carlo

Invited speakers:

Fred Roosta, The University of Queensland: How Randomized Analysis Can Help Us Do “Data Science:” Examples in Deep Learning and Graph Analysis

Chris Drovandi, Queensland University of Technology: Sequential Monte Carlo for Static Bayesian Models

Xuhui Fan, University of New South Wales: Rectangular Bounding Process

Matias Quiroz, University of New South Wales: The block-Poisson estimator for optimally tuned signed pseudo-marginal MCMC

Sarat Moka, The University of Queensland: Perfect Sampling for Gibbs point processes using partial rejection sampling

Rob Salomone, The University of Queensland: An introduction to Stein kernels.

Tui Nolan, University of Technology Sydney: Variational Bayesian Methods for Multilevel Data Models

Gael Martin, Monash University: Loss-based Bayesian Prediction

Organisers: Robert Kohn, UNSW and Dirk Kroese, UQ

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Date and Time

Location

The University of Queensland

Emmanuel College

Riverview Room

Saint Lucia, QLD 4072

Australia

View Map

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