Robert Kohn
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View article: Variational Bayesian inference for models with nuisance parameters and an intractable likelihood
Variational Bayesian inference for models with nuisance parameters and an intractable likelihood Open
A primary challenge in Bayesian analysis lies in computing the posterior distribution of model parameters, especially for models with a large number of parameters or intractable likelihoods. Often, the focus is on a subset of parameters, w…
View article: Calibrated Bayesian inference for random fields on large irregular domains using the debiased spatial Whittle likelihood
Calibrated Bayesian inference for random fields on large irregular domains using the debiased spatial Whittle likelihood Open
Bayesian inference for stationary random fields is computationally demanding. Whittle-type likelihoods in the frequency domain based on the fast Fourier Transform (FFT) have several appealing features: i) low computational complexity of on…
View article: Bayesian inference for evidence accumulation models with regressors.
Bayesian inference for evidence accumulation models with regressors. Open
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. Developments in estimation procedures have helped EAMs become i…
View article: A Beta Cauchy-Cauchy (BECCA) shrinkage prior for Bayesian variable selection
A Beta Cauchy-Cauchy (BECCA) shrinkage prior for Bayesian variable selection Open
This paper introduces a novel Bayesian approach for variable selection in high-dimensional and potentially sparse regression settings. Our method replaces the indicator variables in the traditional spike and slab prior with continuous, Bet…
View article: Spectral domain likelihoods for Bayesian inference in time-varying parameter models
Spectral domain likelihoods for Bayesian inference in time-varying parameter models Open
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 using such a likelihood approximation is that i) it has sub…
View article: Time-Varying Multi-Seasonal AR Models
Time-Varying Multi-Seasonal AR Models Open
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 point and can accommodate multiple seasonal periods. The time…
View article: Dynamic linear regression models for forecasting time series with semi long memory errors
Dynamic linear regression models for forecasting time series with semi long memory errors Open
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 the error term. This error process is often assumed to foll…
View article: Analysing symbolic data by pseudo-marginal methods
Analysing symbolic data by pseudo-marginal methods Open
Symbolic data analysis (SDA) aggregates large individual-level datasets into a small number of distributional summaries, such as random rectangles or random histograms. Inference is carried out using these summaries in place of the origina…
View article: Variational Bayesian Inference for Modelswith Nuisance Parameters and an Intractable Likelihood
Variational Bayesian Inference for Modelswith Nuisance Parameters and an Intractable Likelihood Open
A primary challenge in Bayesian analysis lies in computing the posterior distributionof model parameters, a task that becomes more challenging for models with a largenumber of parameters or when the likelihood is intractable; as seen in Ba…
View article: ProDAG: Projected Variational Inference for Directed Acyclic Graphs
ProDAG: Projected Variational Inference for Directed Acyclic Graphs Open
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, it remains statistically and computationally challenging to …
View article: The Interaction between Credit Constraints and Uncertainty Shocks
The Interaction between Credit Constraints and Uncertainty Shocks Open
This paper proposes a novel link between credit markets and uncertainty shocks. We introduce a role for credit uncertainty via collateral constraints in an otherwise standard real business cycle (RBC) model and show that an increase in cre…
View article: The Block-Correlated Pseudo Marginal Sampler for State Space Models
The Block-Correlated Pseudo Marginal Sampler for State Space Models Open
Pseudo Marginal Metropolis-Hastings (PMMH) is a general approach to Bayesian inference when the likelihood is intractable, but can be estimated unbiasedly. Our article develops an efficient PMMH method that scales up better to higher dimen…
View article: Calibrated Generalized Bayesian Inference
Calibrated Generalized Bayesian Inference Open
We provide a simple and general solution for accurate uncertainty quantification of Bayesian inference in misspecified or approximate models, and for generalized posteriors more generally. While existing solutions are based on explicit Gau…
View article: Contextual Directed Acyclic Graphs
Contextual Directed Acyclic Graphs Open
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 learning a single DAG for the entire population. This paper …
View article: Flexible Variational Bayes Based on a Copula of a Mixture
Flexible Variational Bayes Based on a Copula of a Mixture Open
Variational Bayes methods approximate the posterior density by a family of tractable distributions whose parameters are estimated by optimization. Variational approximation is useful when exact inference is intractable or very costly. Our …
View article: Global Neural Networks and The Data Scaling Effect in Financial Time Series Forecasting
Global Neural Networks and The Data Scaling Effect in Financial Time Series Forecasting Open
Neural networks have revolutionized many empirical fields, yet their application to financial time series forecasting remains controversial. In this study, we demonstrate that the conventional practice of estimating models locally in data-…
View article: Adaptively switching between a particle marginal Metropolis-Hastings and a particle Gibbs kernel in SMC$^2$
Adaptively switching between a particle marginal Metropolis-Hastings and a particle Gibbs kernel in SMC$^2$ Open
Sequential Monte Carlo squared (SMC$^2$; Chopin et al., 2012) methods can be used to sample from the exact posterior distribution of intractable likelihood state space models. These methods are the SMC analogue to particle Markov chain Mon…
View article: Automatically adapting the number of state particles in SMC$$^2$$
Automatically adapting the number of state particles in SMC$$^2$$ Open
Sequential Monte Carlo squared (SMC $$^2$$ ) methods can be used for parameter inference of intractable likelihood state-space models. These methods replace the likelihood with an unbiased particle filter estimate, similarly to particle…
View article: Particle Mean Field Variational Bayes
Particle Mean Field Variational Bayes Open
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 conjugate priors or those that require analytical calculatio…
View article: Bayesian Inference for Evidence Accumulation Models with Regressors
Bayesian Inference for Evidence Accumulation Models with Regressors Open
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. Developments in estimation procedures have helped EAMs become i…
View article: Deep Learning Enhanced Realized GARCH
Deep Learning Enhanced Realized GARCH Open
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 distills modeling advances from financial econometrics, high …
View article: Reliable Bayesian Inference in Misspecified Models
Reliable Bayesian Inference in Misspecified Models Open
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 misspecified models. While existing solutions are based on expli…
View article: Structured variational approximations with skew normal decomposable graphical models
Structured variational approximations with skew normal decomposable graphical models Open
Although there is much recent work developing flexible variational methods for Bayesian computation, Gaussian approximations with structured covariance matrices are often preferred computationally in high-dimensional settings. This paper c…
View article: The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks Open
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. Unfortunately, sparse linear models are far less flexible …
View article: Flexible Variational Bayes Based on a Copula of a Mixture
Flexible Variational Bayes Based on a Copula of a Mixture Open
Variational Bayes methods approximate the posterior density by a family of tractable distributions whose parameters are estimated by optimization. Variational approximation is useful when exact inference is intractable or very costly. Our …
View article: Flexible Variational Bayes based on a Copula of a Mixture
Flexible Variational Bayes based on a Copula of a Mixture Open
Variational Bayes methods approximate the posterior density by a family of tractable distributions whose parameters are estimated by optimisation. Variational approximation is useful when exact inference is intractable or very costly. Our …
View article: A correlated pseudo-marginal approach to doubly intractable problems
A correlated pseudo-marginal approach to doubly intractable problems Open
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 function, whose normalising factor depends on the model para…