no code implementations • 2 Aug 2016 • Xin Zhang, Scott A. Sisson
In this article, we introduce a blocking scheme to the collapsed Gibbs sampler for the LDA model which can, with a theoretical guarantee, improve chain mixing efficiency.
1 code implementation • 19 Jun 2018 • Vincent Chin, David Gunawan, Denzil G. Fiebig, Robert Kohn, Scott A. Sisson
This article considers the problem of estimating a multivariate probit model in a panel data setting with emphasis on sampling a high-dimensional correlation matrix and improving the overall efficiency of the data augmentation approach.
Computation Applications Methodology
no code implementations • 11 Sep 2018 • Boris Beranger, Huan Lin, Scott A. Sisson
We assume that, as with a standard statistical analysis, inference is required at the level of individual-level data.
no code implementations • 27 Sep 2018 • Ming Xu, Matias Quiroz, Robert Kohn, Scott A. Sisson
From this, we show that the marginal variances of the reparameterization gradient estimator are smaller than those of the score function gradient estimator.
1 code implementation • 25 Feb 2019 • David J. Warne, Scott A. Sisson, Christopher Drovandi
We illustrate the potential of SIMD for accelerating Bayesian computations and provide the reader with techniques for exploiting modern massively parallel processing environments using standard tools.
no code implementations • 11 Sep 2019 • Jacob W. Priddle, Scott A. Sisson, David T. Frazier, Christopher Drovandi
Bayesian synthetic likelihood (BSL) is a popular such method that approximates the likelihood function of the summary statistic with a known, tractable distribution -- typically Gaussian -- and then performs statistical inference using standard likelihood-based techniques.
no code implementations • 9 Dec 2019 • Tom Whitaker, Boris Beranger, Scott A. Sisson
Logistic regression models are a popular and effective method to predict the probability of categorical response data.
no code implementations • 24 Feb 2020 • Yaqiong Li, Xuhui Fan, Ling Chen, Bin Li, Zheng Yu, Scott A. Sisson
In this work, we leverage its interpretable modelling architecture and propose a deep dynamic probabilistic framework -- the Recurrent Dirichlet Belief Network~(Recurrent-DBN) -- to study interpretable hidden structures from dynamic relational data.
no code implementations • 25 Feb 2020 • Xuhui Fan, Yaqiong Li, Ling Chen, Bin Li, Scott A. Sisson
We initially propose the Integrated Smoothing Graphon (ISG) which introduces one smoothing parameter to the SBM graphon to generate continuous relational intensity values.
no code implementations • 26 Feb 2020 • Xuhui Fan, Bin Li, Ling Luo, Scott A. Sisson
Bayesian nonparametric space partition (BNSP) models provide a variety of strategies for partitioning a $D$-dimensional space into a set of blocks.
no code implementations • 29 Feb 2020 • Xuhui Fan, Bin Li, Scott A. Sisson
The Binary Space Partitioning-Tree~(BSP-Tree) process was recently proposed as an efficient strategy for space partitioning tasks.
no code implementations • 3 Sep 2020 • Igor Balnozan, Denzil G. Fiebig, Anthony Asher, Robert Kohn, Scott A. Sisson
This article investigates retirement decumulation behaviours using the Grouped Fixed-Effects (GFE) estimator applied to Australian panel data on drawdowns from phased withdrawal retirement income products.
no code implementations • 13 Dec 2021 • Anna Lopatnikova, Minh-Ngoc Tran, Scott A. Sisson
Quantum computers promise to surpass the most powerful classical supercomputers when it comes to solving many critically important practical problems, such as pharmaceutical and fertilizer design, supply chain and traffic optimization, or optimization for machine learning tasks.
1 code implementation • 20 Feb 2023 • Xuhui Fan, Edwin V. Bonilla, Terence J. O'Kane, Scott A. Sisson
However, inference in GPSSMs is computationally and statistically challenging due to the large number of latent variables in the model and the strong temporal dependencies between them.