Search Results for author: Scott A. Sisson

Found 14 papers, 3 papers with code

Blocking Collapsed Gibbs Sampler for Latent Dirichlet Allocation Models

no code implementations2 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.

Blocking

Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives

1 code implementation19 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

New models for symbolic data analysis

no code implementations11 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.

Variance reduction properties of the reparameterization trick

no code implementations27 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.

Variational Inference

Vector operations for accelerating expensive Bayesian computations -- a tutorial guide

1 code implementation25 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.

Distributed Computing

Efficient Bayesian synthetic likelihood with whitening transformations

no code implementations11 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.

Bayesian Inference

Logistic regression models for aggregated data

no code implementations9 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.

Crop Classification General Classification +1

Recurrent Dirichlet Belief Networks for Interpretable Dynamic Relational Data Modelling

no code implementations24 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.

Link Prediction

Smoothing Graphons for Modelling Exchangeable Relational Data

no code implementations25 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.

Link Prediction Stochastic Block Model

Bayesian Nonparametric Space Partitions: A Survey

no code implementations26 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.

Online Binary Space Partitioning Forests

no code implementations29 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.

General Classification regression

Hidden Group Time Profiles: Heterogeneous Drawdown Behaviours in Retirement

no code implementations3 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.

An Introduction to Quantum Computing for Statisticians and Data Scientists

no code implementations13 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.

Free-Form Variational Inference for Gaussian Process State-Space Models

1 code implementation20 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.

Variational Inference

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