Robert Kohn Source Confirmed

Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.

High Impact

Scientia Professor

John Brown University

faculty

45 h-index 423 pubs 10,134 cited

Research Areas

Markov Chains and Monte Carlo Methods Bayesian Methods and Mixture Models Financial Risk and Volatility Modeling Statistical Methods and Inference Statistical Methods and Bayesian Inference

Links

ORCID

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OverviewAI-generated summary

Robert Kohn, a Scientia Professor at John Brown University, concentrates on statistical methods and inference, with a particular emphasis on Bayesian approaches. His research encompasses Markov Chain Monte Carlo methods, Bayesian methods, mixture models, and financial risk/volatility modeling. Kohn's work involves the development and application of advanced statistical techniques to diverse problems. Recent studies focus on Bayesian inference using synthetic likelihoods, along with variational Bayes approximations of factor stochastic volatility models. He also applies his statistical expertise to cognitive modeling, analyzing time-evolving psychological processes and developing efficient methods for model selection using variational Bayes. Kohn's contributions extend to financial modeling, including recurrent stochastic volatility models for stock markets.

Metrics

  • h-index: 45
  • Publications: 423
  • Citations: 10,134

Selected Publications

  • A long short-term memory enhanced realized conditional heteroskedasticity model (2024) DOI
  • Dynamic linear regression models for forecasting time series with semi long memory errors (2024) DOI
  • Particle MCMC and the correlated particle hybrid sampler for state space models (2024) DOI
  • Structured Variational Approximations with Skew Normal Decomposable Graphical Models and Implicit Copulas (2024) DOI
  • The Block-Correlated Pseudo Marginal Sampler for State Space Models (2024) DOI
  • The Interaction between Credit Constraints and Uncertainty Shocks (2024) DOI
  • Flexible Variational Bayes based on a Copula of a Mixture (2023) DOI
  • Flexible Variational Bayes Based on a Copula of a Mixture (2023) DOI
  • Calibrated Generalized Bayesian Inference (2023) DOI
  • Contextual Directed Acyclic Graphs (2023) DOI
  • Flexible Variational Bayes Based on a Copula of a Mixture (2023) DOI
  • Global Neural Networks and The Data Scaling Effect in Financial Time Series Forecasting (2023) DOI
  • Adaptively switching between a particle marginal Metropolis-Hastings and a particle Gibbs kernel in SMC$^2$ (2023) DOI
  • Automatically adapting the number of state particles in SMC$$^2$$ (2023) DOI
  • Particle Mean Field Variational Bayes (2023) DOI

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