Publications (84)
Variational Approximation of Factor Stochastic Volatility Models
David Gunawan, Robert Kohn, David Nott
Estimation and prediction in high dimensional multivariate factor stochastic volatility models is an important and active research area because such models allow a parsimonious rep…
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
Ryan Thompson, Amir Dezfouli, Robert Kohn
Sparse linear models are one of several core tools for interpretable machine learning, a field of emerging importance as predictive models permeate decision-making in many domains.…
Calibrated Bayesian inference for random fields on large irregular domains using the debiased spatial Whittle likelihood
Thomas Goodwin, Arthur Guillaumin, Matias Quiroz +2
Bayesian inference for stationary random fields is computationally demanding. Whittle-type likelihoods in the frequency domain based on the fast Fourier Transform (FFT) have severa…
Robustly estimating the marginal likelihood for cognitive models via importance sampling
Minh-Ngoc Tran, Marcel Scharth, David Gunawan +3
Recent advances in Markov chain Monte Carlo (MCMC) extend the scope of Bayesian inference to models for which the likelihood function is intractable. Although these developments al…
Structured variational approximations with skew normal decomposable graphical models
Robert Salomone, Xuejun Yu, David J. Nott +1
Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred…
Spectral subsampling MCMC for Lévy-driven continuous-time ARMA models with expensive likelihood contributions
Thomas Goodwin, Matias Quiroz, Robert Kohn +1
Subsampling-based Markov chain Monte Carlo (MCMC) algorithms aim to accelerate Bayesian inference by evaluating the likelihood using only a subset of the data at each iteration. Ho…
Variational Bayes with Intractable Likelihood
Minh-Ngoc Tran, David J. Nott, Robert Kohn
Variational Bayes (VB) is rapidly becoming a popular tool for Bayesian inference in statistical modeling. However, the existing VB algorithms are restricted to cases where the like…
Copula-type Estimators for Flexible Multivariate Density Modeling using Mixtures
Minh-Ngoc Tran, Paolo Giordani, Xiuyan Mun +2
Copulas are popular as models for multivariate dependence because they allow the marginal densities and the joint dependence to be modeled separately. However, they usually require…
Auxiliary Particle filtering within adaptive Metropolis-Hastings Sampling
Michael Pitt, Ralph Silva, Paolo Giordani +1
Our article deals with Bayesian inference for a general state space model with the simulated likelihood computed by the particle filter. We show empirically that the partially or f…
Particle filtering within adaptive Metropolis Hastings sampling
Ralph Silva, Paolo Giordani, Robert Kohn +1
We show that it is feasible to carry out exact Bayesian inference for non-Gaussian state space models using an adaptive Metropolis Hastings sampling scheme with the likelihood appr…
Particle Efficient Importance Sampling
Marcel Scharth, Robert Kohn
The efficient importance sampling (EIS) method is a general principle for the numerical evaluation of high-dimensional integrals that uses the sequential structure of target integr…
ProDAG: Projected Variational Inference for Directed Acyclic Graphs
Ryan Thompson, Edwin V. Bonilla, Robert Kohn
Directed acyclic graph (DAG) learning is a central task in structure discovery and causal inference. Although the field has witnessed remarkable advances over the past few years, i…
Bayesian inference using synthetic likelihood: asymptotics and adjustments
David T. Frazier, David J. Nott, Christopher Drovandi +1
Implementing Bayesian inference is often computationally challenging in applications involving complex models, and sometimes calculating the likelihood itself is difficult. Synthet…
Mixed Marginal Copula Modeling
David Gunawan, Mohamad A. Khaled, Robert Kohn
This article extends the literature on copulas with discrete or continuous marginals to the case where some of the marginals are a mixture of discrete and continuous components. We…
Exact ABC using Importance Sampling
Minh Ngoc Tran, Robert Kohn
Approximate Bayesian Computation (ABC) is a powerful method for carrying out Bayesian inference when the likelihood is computationally intractable. However, a drawback of ABC is th…
Robust Particle Density Tempering for State Space Models
David Gunawan, Robert Kohn, Minh Ngoc Tran
Density tempering (also called density annealing) is a sequential Monte Carlo approach to Bayesian inference for general state models; it is an alternative to Markov chain Monte Ca…
Adaptively switching between a particle marginal Metropolis-Hastings and a particle Gibbs kernel in SMC
Imke Botha, Robert Kohn, Leah South +1
Sequential Monte Carlo squared (SMC; Chopin et al., 2012) methods can be used to sample from the exact posterior distribution of intractable likelihood state space models. Thes…
Subsampling MCMC - An introduction for the survey statistician
Matias Quiroz, Mattias Villani, Robert Kohn +2
The rapid development of computing power and efficient Markov Chain Monte Carlo (MCMC) simulation algorithms have revolutionized Bayesian statistics, making it a highly practical i…
The Correlated Particle Hybrid Sampler for State Space Models
David Gunawan, Chris Carter, Robert Kohn
Particle Markov Chain Monte Carlo (PMCMC) is a general computational approach to Bayesian inference for general state space models. Our article scales up PMCMC in terms of the numb…
Time-Varying Multi-Seasonal AR Models
Ganna Fagerberg, Mattias Villani, Robert Kohn
We propose a seasonal AR model with time-varying parameter processes in both the regular and seasonal parameters. The model is parameterized to guarantee stability at every time po…
Bayesian Inference for Evidence Accumulation Models with Regressors
Viet Hung Dao, David Gunawan, Robert Kohn +3
Evidence accumulation models (EAMs) are an important class of cognitive models used to analyze both response time and response choice data recorded from decision-making tasks. Deve…
An extended space approach for particle Markov chain Monte Carlo methods
Christopher K. Carter, Eduardo F. Mendes, Robert Kohn
In this paper we consider fully Bayesian inference in general state space models. Existing particle Markov chain Monte Carlo (MCMC) algorithms use an augmented model that takes int…
Particle Methods for Stochastic Differential Equation Mixed Effects Models
Imke Botha, Robert Kohn, Christopher Drovandi
Parameter inference for stochastic differential equation mixed effects models (SDEMEMs) is a challenging problem. Analytical solutions for these models are rarely available, which…
Time-Varying Multi-Seasonal ARMA Models
Ganna Fagerberg, Mattias Villani, Robert Kohn
We propose an ARMA model that allows for multiple seasonal periods and time varying parameters in both regular and seasonal components, building upon previous work for pure AR proc…
Fast Variational Boosting for Latent Variable Models
David Gunawan, David Nott, Robert Kohn
We consider the problem of estimating complex statistical latent variable models using variational Bayes methods. These methods are used when exact posterior inference is either in…
Scalable MCMC for Large Data Problems using Data Subsampling and the Difference Estimator
Matias Quiroz, Mattias Villani, Robert Kohn
We propose a generic Markov Chain Monte Carlo (MCMC) algorithm to speed up computations for datasets with many observations. A key feature of our approach is the use of the highly…
Efficient Selection Between Hierarchical Cognitive Models: Cross-validation With Variational Bayes
Viet-Hung Dao, David Gunawan, Minh-Ngoc Tran +3
Model comparison is the cornerstone of theoretical progress in psychological research. Common practice overwhelmingly relies on tools that evaluate competing models by balancing in…
Efficient Bayesian estimation for flexible panel models for multivariate outcomes: Impact of life events on mental health and excessive alcohol consumption
David Gunawan, Chris carter, Denzil Fiebig +1
The problem we consider considers estimating a multivariate longitudinal panel data model whose outcomes can be a combination of discrete and continuous variables. This problem is…
A Bayesian changepoint methodology for high dimensional multivariate time series and space-time data: A study of structural change using remotely sensed data
Chris Strickland, Robert Burdett, Robert Denham +2
A Bayesian approach is developed to analyze change points in multivariate time series and space-time data. The methodology is used to assess the impact of extended inundation on th…
A Beta Cauchy-Cauchy (BECCA) shrinkage prior for Bayesian variable selection
Linduni M. Rodrigo, Robert Kohn, Hadi M. Afshar +1
This paper introduces a novel Bayesian approach for variable selection in high-dimensional and potentially sparse regression settings. Our method replaces the indicator variables i…
Efficient variational inference for generalized linear mixed models with large datasets
David J Nott, Minh-Ngoc Tran, Anthony Y. C. Kuk +1
The article develops a hybrid Variational Bayes algorithm that combines the mean-field and fixed-form Variational Bayes methods. The new estimation algorithm can be used to approxi…
On the existence of moments for high dimensional importance sampling
Michael K. Pitt, Minh-Ngoc Tran, Marcel Scharth +1
Theoretical results for importance sampling rely on the existence of certain moments of the importance weights, which are the ratios between the proposal and target densities. In p…
Dynamic linear regression models for forecasting time series with semi long memory errors
Thomas Goodwin, Matias Quiroz, Robert Kohn
Dynamic linear regression models forecast the values of a time series based on a linear combination of a set of exogenous time series while incorporating a time series process for…
A copula based approach to adaptive sampling
Ralph Silva, Robert Kohn, Paolo Giordani +1
Our article is concerned with adaptive sampling schemes for Bayesian inference that update the proposal densities using previous iterates. We introduce a copula based proposal dens…
Calibrated Generalized Bayesian Inference
David T. Frazier, Christopher Drovandi, Robert Kohn
We propose a simple approach that provides accurate uncertainty quantification for Bayesian inference in misspecified or approximate models, and for generalized (Gibbs) posteriors.…
Dynamic Mixture of Experts Models for Online Prediction
Parfait Munezero, Mattias Villani, Robert Kohn
A mixture of experts models the conditional density of a response variable using a mixture of regression models with covariate-dependent mixture weights. We extend the finite mixtu…
Particle Mean Field Variational Bayes
Minh-Ngoc Tran, Paco Tseng, Robert Kohn
The Mean Field Variational Bayes (MFVB) method is one of the most computationally efficient techniques for Bayesian inference. However, its use has been restricted to models with c…
Speeding Up MCMC by Delayed Acceptance and Data Subsampling
Matias Quiroz, Minh-Ngoc Tran, Mattias Villani +1
The complexity of the Metropolis-Hastings (MH) algorithm arises from the requirement of a likelihood evaluation for the full data set in each iteration. Payne and Mallick (2015) pr…
Speeding Up MCMC by Efficient Data Subsampling
Matias Quiroz, Robert Kohn, Mattias Villani +1
We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework where the likelihood function for observations is estimated from a random subset of observations. W…
Bayesian Deep Net GLM and GLMM
Minh-Ngoc Tran, Nghia Nguyen, David Nott +1
Deep feedforward neural networks (DFNNs) are a powerful tool for functional approximation. We describe flexible versions of generalized linear and generalized linear mixed models i…
Modelling age-related changes in executive functions of soccer players
Vincent Chin, Adam Beavan, Job Fransen +4
The widespread popularity of soccer across the globe has turned it into a multi-billion dollar industry. As a result, most professional clubs actively engage in talent identificati…
Bayesian inference for latent factor GARCH models
Michael K. Pitt, Jamie Hall, Robert Kohn
Latent factor GARCH models are difficult to estimate using Bayesian methods because standard Markov chain Monte Carlo samplers produce slowly mixing and inefficient draws from the…
Contextual Directed Acyclic Graphs
Ryan Thompson, Edwin V. Bonilla, Robert Kohn
Estimating the structure of directed acyclic graphs (DAGs) from observational data remains a significant challenge in machine learning. Most research in this area concentrates on l…
Automatically adapting the number of state particles in SMC
Imke Botha, Robert Kohn, Leah South +1
Sequential Monte Carlo squared (SMC) methods can be used for parameter inference of intractable likelihood state-space models. These methods replace the likelihood with an unbi…
Bayesian Covariance Matrix Estimation using a Mixture of Decomposable Graphical Models
Helen Armstrong, Christopher K. Carter, Kevin F. Wong +1
A Bayesian approach is used to estimate the covariance matrix of Gaussian data. Ideas from Gaussian graphical models and model selection are used to construct a prior for the covar…
The Interaction Between Credit Constraints and Uncertainty Shocks
Pratiti Chatterjee, David Gunawan, Robert Kohn
Can uncertainty about credit availability trigger a slowdown in real activity? This question is answered by using a novel method to identify shocks to uncertainty in access to cred…
Importance sampling squared for Bayesian inference in latent variable models
Minh-Ngoc Tran, Marcel Scharth, Michael K. Pitt +1
We consider Bayesian inference by importance sampling when the likelihood is analytically intractable but can be unbiasedly estimated. We refer to this procedure as importance samp…
An energy minimization approach to twinning with variable volume fraction
Sergio Conti, Robert Kohn, Oleksandr Misiats
In materials that undergo martensitic phase transformation, macroscopic loading often leads to the creation and/or rearrangement of elastic domains. This paper considers an example…
Analysing symbolic data by pseudo-marginal methods
Yu Yang, Matias Quiroz, Boris Beranger +2
Symbolic data analysis (SDA) aggregates large individual-level datasets into a small number of distributional summaries, such as random rectangles or random histograms. The inferen…
Global Neural Networks and The Data Scaling Effect in Financial Time Series Forecasting
Chen Liu, Minh-Ngoc Tran, Chao Wang +2
Neural networks have revolutionized many empirical fields, yet their application to financial time series forecasting remains controversial. In this study, we demonstrate that the…
Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives
Vincent Chin, David Gunawan, Denzil G. Fiebig +2
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 t…
New Estimation Approaches for the Hierarchical Linear Ballistic Accumulator Model
David Gunawan, Guy E. Hawkins, Minh-Ngoc Tran +2
The Linear Ballistic Accumulator (Brown & Heathcote, 2008) model is used as a measurement tool to answer questions about applied psychology. The analyses based on this model depend…
Adaptive Metropolis-Hastings Sampling using Reversible Dependent Mixture Proposals
Minh-Ngoc Tran, Michael K. Pitt, Robert Kohn
This article develops a general-purpose adaptive sampler that approximates the target density by a mixture of multivariate t densities. The adaptive sampler is based on reversible…
Gaussian variational approximation for high-dimensional state space models
Matias Quiroz, David J. Nott, Robert Kohn
Our article considers a Gaussian variational approximation of the posterior density in a high-dimensional state space model. The variational parameters to be optimized are the mean…
Computationally Efficient Estimation of Factor Multivariate Stochastic Volatility Models
Weijun Xu, Li Yang, Robert Kohn
An MCMC simulation method based on a two stage delayed rejection Metropolis-Hastings algorithm is proposed to estimate a factor multivariate stochastic volatility model. The first…
Variance reduction properties of the reparameterization trick
Ming Xu, Matias Quiroz, Robert Kohn +1
The reparameterization trick is widely used in variational inference as it yields more accurate estimates of the gradient of the variational objective than alternative approaches s…
Markov Interacting Importance Samplers
Eduardo F. Mendes, Marcel Scharth, Robert Kohn
We introduce a new Markov chain Monte Carlo (MCMC) sampler called the Markov Interacting Importance Sampler (MIIS). The MIIS sampler uses conditional importance sampling (IS) appro…
A correlated pseudo-marginal approach to doubly intractable problems
Yu Yang, Matias Quiroz, Robert Kohn +1
Doubly intractable models are encountered in a number of fields, e.g. social networks, ecology and epidemiology. Inference for such models requires the evaluation of a likelihood f…
Flexible Variational Bayes based on a Copula of a Mixture
David Gunawan, Robert Kohn, David Nott
Variational Bayes methods approximate the posterior density by a family of tractable distributions whose parameters are estimated by optimisation. Variational approximation is usef…
Reliable Bayesian Inference in Misspecified Models
David T. Frazier, Robert Kohn, Christopher Drovandi +1
We provide a general solution to a fundamental open problem in Bayesian inference, namely poor uncertainty quantification, from a frequency standpoint, of Bayesian methods in missp…
On approximating copulas by finite mixtures
Mohamad A. Khaled, Robert Kohn
Copulas are now frequently used to construct or estimate multivariate distributions because of their ability to take into account the multivariate dependence of the different varia…
Locally Adaptive Nonparametric Binary Regression
Sally Wood, Robert Kohn, Remy Cottet +2
A nonparametric and locally adaptive Bayesian estimator is proposed for estimating a binary regression. Flexibility is obtained by modeling the binary regression as a mixture of pr…
A flexible Particle Markov chain Monte Carlo method
Eduardo F. Mendes, Christopher K. Carter, David Gunawan +1
Particle Markov Chain Monte Carlo methods are used to carry out inference in non-linear and non-Gaussian state space models, where the posterior density of the states is approximat…
Hidden Group Time Profiles: Heterogeneous Drawdown Behaviours in Retirement
Igor Balnozan, Denzil G. Fiebig, Anthony Asher +2
This article investigates retirement decumulation behaviours using the Grouped Fixed-Effects (GFE) estimator applied to Australian panel data on drawdowns from phased withdrawal re…
Bayesian inference for nonlinear structural time series models
Jamie Hall, Michael K. Pitt, Robert Kohn
This article discusses a partially adapted particle filter for estimating the likelihood of a nonlinear structural econometric state space models whose state transition density can…
Flexible Multivariate Density Estimation with Marginal Adaptation
Paolo Giordani, Xiuyan Mun, Robert Kohn
Our article addresses the problem of flexibly estimating a multivariate density while also attempting to estimate its marginals correctly. We do so by proposing two new estimators…
Dynamic models using score copula innovations
Landan Zhang, Michael K. Pitt, Robert Kohn
This paper introduces a new class of observation driven dynamic models. The time evolving parameters are driven by innovations of copula form. The resulting models can be made stri…
Subsampling Sequential Monte Carlo for Static Bayesian Models
David Gunawan, Khue-Dung Dang, Matias Quiroz +2
We show how to speed up Sequential Monte Carlo (SMC) for Bayesian inference in large data problems by data subsampling. SMC sequentially updates a cloud of particles through a sequ…
Time-evolving psychological processes over repeated decisions
David Gunawan, Guy E. Hawkins, Robert Kohn +2
Many psychological experiments have subjects repeat a task to gain the statistical precision required to test quantitative theories of psychological performance. In such experiment…
The block-Poisson estimator for optimally tuned exact subsampling MCMC
Matias Quiroz, Minh-Ngoc Tran, Mattias Villani +2
Speeding up Markov Chain Monte Carlo (MCMC) for datasets with many observations by data subsampling has recently received considerable attention. A pseudo-marginal MCMC method is p…
Spectral domain likelihoods for Bayesian inference in time-varying parameter models
Oskar Gustafsson, Mattias Villani, Robert Kohn
Inference for locally stationary processes is often based on some local Whittle-type approximation of the likelihood function defined in the frequency domain. The main reasons for…
Deep Learning Enhanced Realized GARCH
Chen Liu, Chao Wang, Minh-Ngoc Tran +1
We propose a new approach to volatility modeling by combining deep learning (LSTM) and realized volatility measures. This LSTM-enhanced realized GARCH framework incorporates and di…
A Copula Based Bayesian Approach for Paid-Incurred Claims Models for Non-Life Insurance Reserving
Gareth W. Peters, Alice X. D. Dong, Robert Kohn
Our article considers the class of recently developed stochastic models that combine claims payments and incurred losses information into a coherent reserving methodology. In parti…
Efficient Bayesian inference for multivariate factor stochastic volatility models with leverage
David Gunawan, Chris Carter, Robert Kohn
This paper discusses the efficient Bayesian estimation of a multivariate factor stochastic volatility (Factor MSV) model with leverage. We propose a novel approach to construct the…
A flexible observed factor model with separate dynamics for the factor volatilities and their correlation matrix
Yu-Cheng Ku, Peter Bloomfield, Robert Kohn
Our article considers a regression model with observed factors. The observed factors have a flexible stochastic volatility structure that has separate dynamics for the volatilities…
Spectral Subsampling MCMC for Stationary Time Series
Robert Salomone, Matias Quiroz, Robert Kohn +2
Bayesian inference using Markov Chain Monte Carlo (MCMC) on large datasets has developed rapidly in recent years. However, the underlying methods are generally limited to relativel…
Variable Selection and Model Averaging in Semiparametric Overdispersed Generalized Linear Models
Remy Cottet, Robert Kohn, David Nott
We express the mean and variance terms in a double exponential regression model as additive functions of the predictors and use Bayesian variable selection to determine which predi…
Identifying relationships between cognitive processes across tasks, contexts, and time
Laura Wall, David Gunawan, Scott D. Brown +3
It is commonly assumed that a specific testing occasion (task, design, procedure, etc.) provides insights that generalise beyond that occasion. This assumption is infrequently care…
Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
Arnaud Doucet, Michael Pitt, George Deligiannidis +1
When an unbiased estimator of the likelihood is used within a Metropolis--Hastings chain, it is necessary to trade off the number of Monte Carlo samples used to construct this esti…
Spectral Subsampling MCMC for Stationary Multivariate Time Series with Applications to Vector ARTFIMA Processes
Mattias Villani, Matias Quiroz, Robert Kohn +1
Spectral subsampling MCMC was recently proposed to speed up Markov chain Monte Carlo (MCMC) for long stationary univariate time series by subsampling periodogram observations in th…
The Block-Correlated Pseudo Marginal Sampler for State Space Models
David Gunawan, Pratiti Chatterjee, Robert Kohn
Particle Marginal Metropolis-Hastings (PMMH) is a general approach to Bayesian inference when the likelihood is intractable, but can be estimated unbiasedly. Our article develops a…
Hamiltonian Monte Carlo with Energy Conserving Subsampling
Khue-Dung Dang, Matias Quiroz, Robert Kohn +2
Hamiltonian Monte Carlo (HMC) samples efficiently from high-dimensional posterior distributions with proposed parameter draws obtained by iterating on a discretized version of the…
Multiclass classification of growth curves using random change points and heterogeneous random effects
Vincent Chin, Jarod Y. L. Lee, Louise M. Ryan +2
Faltering growth among children is a nutritional problem prevalent in low to medium income countries; it is generally defined as a slower rate of growth compared to a reference hea…
Fast Inference for Intractable Likelihood Problems using Variational Bayes
David Gunawan, Minh-Ngoc Tran, Robert Kohn
Variational Bayes (VB) is a popular estimation method for Bayesian inference. However, most existing VB algorithms are restricted to cases where the likelihood is tractable, which…