Robert Kohn

is this you? claim profile

∙ 0 followers

Scientia Professor School of Economics - PhD, Econometrics

Efficient Selection Between Hierarchical Cognitive Models: Cross-validation With Variational Bayes

Model comparison is the cornerstone of theoretical progress in psycholog...
02/12/2021 ∙ by Viet-Hung Dao, et al. ∙ 0 ∙ share

read it
Variational Approximation of Factor Stochastic Volatility Models

Estimation and prediction in high dimensional multivariate factor stocha...
10/13/2020 ∙ by David Gunawan, et al. ∙ 0 ∙ share

read it
Spectral Subsampling MCMC for Stationary Time Series

Bayesian inference using Markov Chain Monte Carlo (MCMC) on large datase...
10/30/2019 ∙ by Robert Salomone, et al. ∙ 0 ∙ share

read it
Identifying relationships between cognitive processes across tasks, contexts, and time

It is commonly assumed that a specific testing occasion (task, design, p...
10/16/2019 ∙ by Laura Wall, et al. ∙ 0 ∙ share

read it
Multiclass classification of growth curves using random change points and heterogeneous random effects

Faltering growth among children is a nutritional problem prevalent in lo...
09/17/2019 ∙ by Vincent Chin, et al. ∙ 0 ∙ share

read it
Particle Methods for Stochastic Differential Equation Mixed Effects Models

Parameter inference for stochastic differential equation mixed effects m...
07/25/2019 ∙ by Imke Botha, et al. ∙ 0 ∙ share

read it
Time-evolving psychological processes over repeated decisions

Many psychological experiments have participants repeat a simple task. T...
06/26/2019 ∙ by David Gunawan, et al. ∙ 0 ∙ share

read it
Robustly estimating the marginal likelihood for cognitive models via importance sampling

Recent advances in Markov chain Monte Carlo (MCMC) extend the scope of B...
06/14/2019 ∙ by Minh-Ngoc Tran, et al. ∙ 0 ∙ share

read it
Bayesian inference using synthetic likelihood: asymptotics and adjustments

Implementing Bayesian inference is often computationally challenging in ...
02/13/2019 ∙ by David J. Nott, et al. ∙ 0 ∙ share

read it
Variance reduction properties of the reparameterization trick

The reparameterization trick is widely used in variational inference as ...
09/27/2018 ∙ by Ming Xu, et al. ∙ 0 ∙ share

read it
On some variance reduction properties of the reparameterization trick

The so-called reparameterization trick is widely used in variational inf...
09/27/2018 ∙ by Ming Xu, et al. ∙ 0 ∙ share

read it
Subsampling MCMC - An introduction for the survey statistician

The rapid development of computing power and efficient Markov Chain Mont...
07/23/2018 ∙ by Matias Quiroz, et al. ∙ 0 ∙ share

read it
Subsampling MCMC - A review for the survey statistician

The rapid development of computing power and efficient Markov Chain Mont...
07/23/2018 ∙ by Matias Quiroz, et al. ∙ 0 ∙ share

read it
New Estimation Approaches for the Linear Ballistic Accumulator Model

The Linear Ballistic Accumulator (LBA) model of Brown (2008) is used as ...
06/26/2018 ∙ by David Gunawan, et al. ∙ 0 ∙ share

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

This article considers the problem of estimating a multivariate probit m...
06/19/2018 ∙ by Vincent Chin, et al. ∙ 0 ∙ share

read it
Bayesian Deep Net GLM and GLMM

Deep feedforward neural networks (DFNNs) are a powerful tool for functio...
05/25/2018 ∙ by Minh-Ngoc Tran, et al. ∙ 0 ∙ share

read it
Subsampling Sequential Monte Carlo for Static Bayesian Models

Our article shows how to carry out Bayesian inference by combining data ...
05/08/2018 ∙ by David Gunawan, et al. ∙ 0 ∙ share

read it
Flexible Density Tempering Approaches for State Space Models with an Application to Factor Stochastic Volatility Models

Duan (2015) propose a tempering or annealing approach to Bayesian infere...
05/02/2018 ∙ by David Gunawan, et al. ∙ 0 ∙ share

read it
Efficiently Combining Pseudo Marginal and Particle Gibbs Sampling

Particle Markov Chain Monte Carlo methods are used to carry out inferenc...
04/12/2018 ∙ by David Gunawan, et al. ∙ 0 ∙ share

read it
Gaussian variational approximation for high-dimensional state space models

Our article considers variational approximations of the posterior distri...
01/24/2018 ∙ by Matias Quiroz, et al. ∙ 0 ∙ share

read it
Hamiltonian Monte Carlo with Energy Conserving Subsampling

Hamiltonian Monte Carlo (HMC) has recently received considerable attenti...
08/02/2017 ∙ by Khue-Dung Dang, et al. ∙ 0 ∙ share

read it
Exact Subsampling MCMC

Speeding up Markov Chain Monte Carlo (MCMC) for data sets with many obse...
03/27/2016 ∙ by Matias Quiroz, et al. ∙ 0 ∙ share

read it
Speeding Up MCMC by Efficient Data Subsampling

We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework...
04/16/2014 ∙ by Matias Quiroz, et al. ∙ 0 ∙ share

read it

Robert Kohn

Featured Co-authors

Minh-Ngoc Tran

Scott A. Sisson

Mattias Villani

Christopher Drovandi

David J. Nott

Ming Xu

David Gunawan

Matias Quiroz

Louise M. Ryan

Robert Salomone

Khue-Dung Dang

Efficient Selection Between Hierarchical Cognitive Models: Cross-validation With Variational Bayes

Variational Approximation of Factor Stochastic Volatility Models

Spectral Subsampling MCMC for Stationary Time Series

Identifying relationships between cognitive processes across tasks, contexts, and time

Multiclass classification of growth curves using random change points and heterogeneous random effects

Particle Methods for Stochastic Differential Equation Mixed Effects Models

Time-evolving psychological processes over repeated decisions

Robustly estimating the marginal likelihood for cognitive models via importance sampling

Bayesian inference using synthetic likelihood: asymptotics and adjustments

Variance reduction properties of the reparameterization trick

On some variance reduction properties of the reparameterization trick

Subsampling MCMC - An introduction for the survey statistician

Subsampling MCMC - A review for the survey statistician

New Estimation Approaches for the Linear Ballistic Accumulator Model

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

Bayesian Deep Net GLM and GLMM

Subsampling Sequential Monte Carlo for Static Bayesian Models

Flexible Density Tempering Approaches for State Space Models with an Application to Factor Stochastic Volatility Models

Efficiently Combining Pseudo Marginal and Particle Gibbs Sampling

Gaussian variational approximation for high-dimensional state space models

Hamiltonian Monte Carlo with Energy Conserving Subsampling

Exact Subsampling MCMC

Speeding Up MCMC by Efficient Data Subsampling