Nicholas G. Polson

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Deep Fundamental Factor Models pdf

Matthew F. Dixon, Nicholas G. Polson, M. Dixon, 2019, ArXiv.

Short Communication: Deep Fundamental Factor Models

Nicholas G. Polson, Matthew F. Dixon, M. Dixon, 2020, SIAM J. Financial Math..

Particle Learning for Sequential Bayesian Computation

Carlos M. Carvalho, Nicholas G. Polson, Hedibert F. Lopes, 2012 .

Nonlinear Filtering of Stochastic Differential Equations with Jumps

Nicholas G. Polson, Michael Johannes, Jonathan R. Stroud, 2002 .

The Bayesian bridge

James G. Scott, Jesse Windle, Nicholas G. Polson, 2011, 1109.2279.

Sequential Regression Trees for Learning and Design

Robert B. Gramacy, Matt Taddy, Nicholas G. Polson, 2009 .

Dynamic Trees for Learning and Design

Robert B. Gramacy, Nicholas G. Polson, Matthew A. Taddy, 2009, 0912.1586.

Deep Portfolio Theory pdf

Nicholas G. Polson, J. B. Heaton, J. B. Heaton, 2016, ArXiv.

Proximal Algorithms in Statistics and Machine Learning pdf

James G. Scott, Nicholas G. Polson, Brandon T. Willard, 2015, ArXiv.

Deep Learning for Finance: Deep Portfolios

Jan Hendrik Witte, Nicholas G. Polson, J. B. Heaton, 2016 .

A simulation-based approach to stochastic dynamic programming

Morten Sorensen, Nicholas G. Polson, M. Sorensen, 2011 .

Deep Learning pdf

Nicholas G. Polson, Vadim O. Sokolov, Vadim O. Sokolov, 2018, Wiley StatsRef: Statistics Reference Online.

From Least Squares to Signal Processing and Particle Filtering

Nozer D. Singpurwalla, Refik Soyer, Nicholas G. Polson, 2017, Technometrics.

Rejoinder: "Data augmentation for support vector machines"

Steven L. Scott, Nicholas G. Polson, S. L. Scott, 2011 .

Deep learning for spatio‐temporal modeling: Dynamic traffic flows and high frequency trading

Nicholas G. Polson, Matthew F. Dixon, Vadim O. Sokolov, 2017, Applied Stochastic Models in Business and Industry.

Vertical-likelihood Monte Carlo Integration

James G. Scott, Nicholas G. Polson, 2014 .

Analysis of economic data with multiscale spatio-temporal models

Nicholas G. Polson, Hedibert F. Lopes, 2018 .

Augmented probability simulation for accelerated life test design

Refik Soyer, Nicholas G. Polson, R. Soyer, 2017 .

Deep Learning in Characteristics-Sorted Factor Models

Nicholas G. Polson, Guanhao Feng, Jianeng Xu, 2020 .

Scalable Data Augmentation for Deep Learning pdf

Nicholas G. Polson, Vadim O. Sokolov, Yuexi Wang, 2019, ArXiv.

Particle Learning in Nonlinear Models using Slice Variables

Nicholas G. Polson, Michael Johannes, Seung M. Yae, 2009 .

Default Bayesian Analysis with Global-Local Shrinkage Priors

Nicholas G. Polson, Anindya Bhadra, Jyotishka Datta, 2015, 1510.03516.

Lasso Meets Horseshoe: A Survey

Nicholas G. Polson, Anindya Bhadra, Jyotishka Datta, 2017, Statistical Science.

Mixtures, envelopes and hierarchical duality

James G. Scott, Nicholas G. Polson, 2014, 1406.0177.

Regularization via Data Augmentation

James G. Scott, Nicholas G. Polson, 2011 .

A Bellman View of Jesse Livermore

Jan Hendrik Witte, Nicholas G. Polson, 2014 .

Bayesian Inference for Logistic Models Using Pólya–Gamma Latent Variables

James G. Scott, Jesse Windle, Nicholas G. Polson, 2012, 1205.0310.

Computations in Bayesian Design of Life Tests

Refik Soyer, Nicholas G. Polson, 2009 .

Tracking Epidemics With Google Flu Trends Data and a State-Space SEIR Model

Nicholas G. Polson, Hedibert F. Lopes, Vanja Dukic, 2012, Journal of the American Statistical Association.

Bayesian Theory and Applications

Petros Dellaportas, Paul Damien, Nicholas G. Polson, 2013 .

Deep Learning Predictors for Traffic Flows

Nicholas G. Polson, Vadim Sokolov, Vadim O. Sokolov, 2016 .

Deep Learning for Predicting Asset Returns pdf

Jingyu He, Nicholas G. Polson, Guanhao Feng, 2018, ArXiv.

Predictive Macro-Finance With Dynamic Partition Models

Paul Damien, Nicholas G. Polson, Daniel Zantedeschi, 2011 .

Discussion on ‘Adversarial risk analysis: Borel games’

Nicholas G. Polson, 2011 .

Handling Sparsity via the Horseshoe

James G. Scott, Carlos M. Carvalho, Nicholas G. Polson, 2009, AISTATS.

Horseshoe Regularisation for Machine Learning in Complex and Deep Models 1

Yunfan Li, Nicholas G. Polson, Anindya Bhadra, 2019, International Statistical Review.

Augmented Markov Chain Monte Carlo Simulation for Two-Stage Stochastic Programs with Recourse

Tahir Ekin, Refik Soyer, Nicholas G. Polson, 2014, Decis. Anal..

Particle Learning of Gaussian Process Models for Sequential Design and Optimization

Robert B. Gramacy, Nicholas G. Polson, R. Gramacy, 2009, 0909.5262.

Bayesian l 0 ‐regularized least squares

Lei Sun, Nicholas G. Polson, Lei Sun, 2017, Applied Stochastic Models in Business and Industry.

Bayesian Analysis of Stochastic Volatility Models: Comments: Reply

Peter E. Rossi, Nicholas G. Polson, Eric Jacquier, 1994 .

Bayesian regularization: From Tikhonov to horseshoe

Nicholas G. Polson, Vadim Sokolov, Vadim O. Sokolov, 2019, WIREs Computational Statistics.

The Horseshoe-Like Regularization for Feature Subset Selection

Nicholas G. Polson, Anindya Bhadra, Jyotishka Datta, 2019, Sankhya B.

Deep Learning in Finance pdf

Nicholas G. Polson, J. B. Heaton, J. B. Heaton, 2016, ArXiv.

Prediction Risk for the Horseshoe Regression

Nicholas G. Polson, Anindya Bhadra, Yunfan Li, 2016, J. Mach. Learn. Res..

On the Geometric Convergence of the Gibbs Sampler

Nicholas G. Polson, G. Roberts, 1994 .

The horseshoe estimator for sparse signals

James G. Scott, Nicholas G. Polson, C. Carvalho, 2010 .

Local shrinkage rules, Lévy processes and regularized regression

James G. Scott, Nicholas G. Polson, 2010, 1010.3390.

Simulation-based Regularized Logistic Regression

Nicholas G. Polson, R. Gramacy, 2010, 1005.3430.

A representation of the posterior mean for a location model

Nicholas G. Polson, 1991 .

On the half-cauchy prior for a global scale parameter

James G. Scott, Nicholas G. Polson, 2011, 1104.4937.

A Monte Carlo Approach to Nonnormal and Nonlinear State-Space Modeling

Nicholas G. Polson, B. Carlin, D. Stoffer, 1992 .

Bayesian Portfolio Selection: An Empirical Analysis of the S&P 500 Index 1970–1996

Nicholas G. Polson, B. V. Tew, 2000 .

Bayesian Analysis of Stochastic Volatility Models

Peter E. Rossi, Nicholas G. Polson, E. Jacquier, 1994 .

Particle Filtering

Nicholas G. Polson, Michael S. Johannes, M. Johannes, 2006 .

Bayesian analysis of traffic flow on interstate I-55: The LWR model

Nicholas G. Polson, Vadim O. Sokolov, 2014, 1409.6034.

Particle Learning and Smoothing

Nicholas G. Polson, Michael S. Johannes, C. Carvalho, 2010, 1011.1098.

MCMC Methods for Financial Econometrics

Nicholas G. Polson, Michael S. Johannes, M. Johannes, 2002 .

Inference for nonconjugate Bayesian Models using the Gibbs sampler

Nicholas G. Polson, B. Carlin, 1991 .

Rejoinder to ‘Deep learning for finance: deep portfolios’: REJOINDER

Nicholas G. Polson, J. B. Heaton, J. Witte, 2017 .

Evidence for and the Impact of Jumps in Volatility and Returns

Nicholas G. Polson, Bjørn Eraker, M. Johannes, 2001 .

Bayes factors for discrete observations from diffusion processes

Nicholas G. Polson, G. Roberts, 1994 .

Diagnostic Measures for Model Criticism

Nicholas G. Polson, G. Parmigiani, C. Carota, 1996 .

Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction

James G. Scott, M. J. Bayarri, Nicholas G. Polson, 2022 .

Bayesian analysis of stochastic volatility models with fat-tails and correlated errors

Peter E. Rossi, Nicholas G. Polson, E. Jacquier, 2004 .

The Impact of Jumps in Volatility and Returns

Nicholas G. Polson, Michael S. Johannes, Bjørn Eraker, 2000 .

Stochastic Volatility: Univariate and Multivariate Extensions

Peter E. Rossi, Nicholas G. Polson, E. Jacquier, 1999 .

State Space and Unobserved Component Models: Practical filtering for stochastic volatility models

Nicholas G. Polson, Peter Müller, J. R. Stroud, 2004 .

Optimal Filtering of Jump Diffusions: Extracting Latent States from Asset Prices

Nicholas G. Polson, Jonathan R. Stroud, Michael S. Johannes, 2009 .

Particle learning for general mixtures

Nicholas G. Polson, C. Carvalho, H. Lopes, 2010 .

Deep learning for finance: deep portfolios: J. B. HEATON, N. G. POLSON AND J. H. WITTE

Nicholas G. Polson, J. B. Heaton, J. Witte, 2017 .

Augmented nested sampling for stochastic programs with recourse and endogenous uncertainty

Nicholas G. Polson, R. Soyer, Tahir Ekin, 2017 .

A Statistical Theory of Deep Learning via Proximal Splitting

Nicholas G. Polson, Brandon T. Willard, Massoud Heidari, 2015, 1509.06061.

Corporate Credit Spreads under Parameter Uncertainty

Nicholas G. Polson, Nick Polson, Arthur Korteweg, 2009 .

Nonlinear State-Space Models With State-Dependent Variances

Nicholas G. Polson, Jonathan R. Stroud, Peter Müller, 2003 .

The Horseshoe+ Estimator of Ultra-Sparse Signals

Nicholas G. Polson, Brandon T. Willard, A. Bhadra, 2015, 1502.00560.

Ineligibles and eligible non-participants as a double comparison group in regression-discontinuity designs

Nicholas G. Polson, Rosa L. Matzkin, J. MacKinnon, 2018 .

Practical filtering with sequential parameter learning

Nicholas G. Polson, Jonathan R. Stroud, Peter Müller, 2008 .

Bayesian Methods In Finance

Nicholas G. Polson, E. Jacquier, Eric Jacquier, 2011 .

Rejoinder to “Sequential Bayesian learning for stochastic volatility with variance-gamma jumps in returns” Reply to the discussions by Nalini Ravishanker and Refik Soyer

Nicholas G. Polson, H. Lopes, Samir P. Warty, 2018, Applied Stochastic Models in Business and Industry.

Sequential Learning, Predictability, and Optimal Portfolio Returns: Sequential Learning, Predictability, and Optimal Portfolio Returns

Nicholas G. Polson, Michael S. Johannes, Arthur Korteweg, 2014 .

MCMC maximum likelihood for latent state models

Nicholas G. Polson, Michael S. Johannes, E. Jacquier, 2007 .

Bayesian Computation in Finance

Nicholas G. Polson, Michael S. Johannes, R. McCulloch, 2010 .

Particle Filtering and Parameter Learning

Nicholas G. Polson, Michael S. Johannes, M. Johannes, 2007 .

Exact Particle Filtering and Parameter Learning

Nicholas G. Polson, Michael S. Johannes, M. Johannes, 2006 .

Weighted Bayesian bootstrap for scalable posterior distributions

Nicholas G. Polson, M. Newton, Jianeng Xu, 2020, Canadian Journal of Statistics.

Weighted Bayesian Bootstrap for Scalable Bayes

Nicholas G. Polson, M. Newton, Jianeng Xu, 2018, 1803.04559.

Split Sampling: Expectations, Normalisation and Rare Events

Nicholas G. Polson, J. Birge, Changgee Chang, 2012, 1212.0534.

Vertical-likelihood Monte Carlo

James G. Scott, Nicholas G. Polson, 2014, 1409.3601.

Global-Local Mixtures

Nicholas G. Polson, Anindya Bhadra, Jyotishka Datta, 2016, 1604.07487.

Sequential Bayesian Analysis of Multivariate Count Data

Tevfik Aktekin, Refik Soyer, Nicholas G. Polson, 2016, Bayesian Analysis.

Sequential Learning, Predictability, and Optimal Portfolio Returns

Nicholas G. Polson, Michael S. Johannes, Nick Polson, 2013 .

Global-Local Mixtures: A Unifying Framework

Nicholas G. Polson, Anindya Bhadra, Jyotishka Datta, 2020, Sankhya A.

Good, great, or lucky? Screening for firms with sustained superior performance using heavy-tailed priors

James G. Scott, Nicholas G. Polson, 2010, 1010.5223.

Bayesian perspectives on statistical modelling

Nicholas G. Polson, 1988 .

A deconvolution path for mixtures

James G. Scott, Nicholas G. Polson, Oscar Hernan Madrid Padilla, 2015, 1511.06750.

Lasso Meets Horseshoe

Nicholas G. Polson, Brandon T. Willard, A. Bhadra, 2017 .

Prior distributions for the bivariate binomial

Larry Wasserman, Nicholas G. Polson, 1990 .

Alternative Global – Local Shrinkage Priors Using Hypergeometric – Beta Mixtures

James G. Scott, Nicholas G. Polson, 2009 .

Bayesian hypothesis testing: Redux

Nicholas G. Polson, Hedibert F. Lopes, H. Lopes, 2018, Brazilian Journal of Probability and Statistics.

Optimisation via Slice Sampling

John R. Birge, Nicholas G. Polson, J. Birge, 2012, 1212.2135.

Deep Factor Alpha

Nicholas G. Polson, Guanhao Feng, Jianeng Xu, 2018 .

Prediction risk for global-local shrinkage regression

Nicholas G. Polson, Anindya Bhadra, Jyotishka Datta, 2016 .

A Bayesian analysis of the multinomial probit model with fully identified parameters

Peter E. Rossi, Robert E. McCulloch, Nicholas G. Polson, 2000 .

Tracking Flu Epidemics Using Google Flu Trends and Particle Learning

Nicholas G. Polson, Hedibert F. Lopes, Vanja Dukic, 2009 .

Large-scale simultaneous testing with hypergeometric inverted-beta priors

James G. Scott, Nicholas G. Polson, 2010 .

A family of multivariate non‐gaussian time series models

Nicholas G. Polson, R. Soyer, Tevfik Aktekin, 2020, Journal of Time Series Analysis.

Particle Learning for Fat-Tailed Distributions

Nicholas G. Polson, H. Lopes, 2016 .

CHAPTER ON BAYESIAN INFERENCE FOR STOCHASTIC VOLATILITY MODELING

Nicholas G. Polson, H. Lopes, 2010 .

Tracking Epidemics with State-space SEIR and Google Flu Trends

Nicholas G. Polson, H. Lopes, Vanja Duki, 2009 .

Bayesian estimation of nonlinear equilibrium models with random coefficients

Nicholas G. Polson, V. Viard, A. Gron, 2015 .

Markov Chain Monte Carlo

Nicholas G. Polson, Michael S. Johannes, 2009 .

Sampling P olya-Gamma random variates: alternate and approximate techniques

James G. Scott, Jesse Windle, Nicholas G. Polson, 2014, 1405.0506.

Diagnostic Measures for Model

Nicholas G. Polson, G. Parmigiani, CriticismCinzia Carota, 1993 .

Bayesian statistics with a smile: A resampling-sampling perspective

Carlos M. Carvalho, Nicholas G. Polson, Hedibert F. Lopes, 2012 .

Sequential Parameter Estimation in Stochastic Volatility Models with Jumps

Nicholas G. Polson, Michael Johannes, Jonathan R. Stroud, 2006 .

Inverse Probability Weighting: the Missing Link between Survey Sampling and Evidence Estimation

Nicholas G. Polson, J. Datta, 2022, 2204.14121.

Sequential Bayesian Analysis of Multivariate Poisson Count Data

Nicholas G. Polson, R. Soyer, Tevfik Aktekin, 2016 .

Bayesian Inference for Polya Inverse Gamma Models.

Nicholas G. Polson, C. Glynn, Jingyu He, 2019 .

Models and Priors for Multivariate Stochastic Volatility

Peter E. Rossi, Nicholas G. Polson, Eric Jacquier, 1995 .

Particle Learning for Fat-tailed Distributions 1

Nicholas G. Polson, H. Lopes, 2010 .

On the Expected Amount of Information from a Non‐Linear Model

Nicholas G. Polson, 1992 .

Explosive Volatility: A Model of Financial Contagion

James G. Scott, Nicholas G. Polson, 2011 .

Sequential Bayesian learning for stochastic volatility with variance‐gamma jumps in returns

Nicholas G. Polson, Hedibert F. Lopes, Samir P. Warty, 2018 .

Bayesian Instrumental Variables: Priors and Likelihoods

Nicholas G. Polson, Hedibert F. Lopes, H. Lopes, 2014 .

Asset Allocation in Finance: A Bayesian Perspective

Nicholas G. Polson, Eric Jacquier, E. Jacquier, 2011 .

The implied volatility of a sports game

Nicholas G. Polson, H. Stern, 2015 .

Regularizing Bayesian predictive regressions

Nicholas G. Polson, Guanhao Feng, 2016, Journal of Asset Management.

Data Augementation with Polya Inverse Gamma

Nicholas G. Polson, Jingyu He, Jianeng Xu, 2019, 1905.12141.

Deep Partial Least Squares for Empirical Asset Pricing

Nicholas G. Polson, M. Dixon, Kemen Goicoechea, 2022, SSRN Electronic Journal.

Testing for unit root processes in random coefficient autoregressive models

Nicholas G. Polson, Rosa L. Matzkin, J. MacKinnon, 2019 .

Sequential Bayesian Learning for Merton's Jump Model with Stochastic Volatility

Nicholas G. Polson, E. Jacquier, Vadim O. Sokolov, 2016, 1610.09750.

Deep Partial Least Squares for IV Regression

Nicholas G. Polson, Vadim O. Sokolov, Maria Nareklishvili, 2022 .

Internet Appendix for : Sequential Learning , Predictability , and Optimal Portfolio Returns

Nicholas G. Polson, Michael S. Johannes, Arthur Korteweg, 2013 .

Distribution free specification tests of conditional models

Nicholas G. Polson, Rosa L. Matzkin, J. MacKinnon, 2018 .

Data augmentation for support vector machines

Nicholas G. Polson, S. L. Scott, 2011 .

Nonparametric simultaneous testing for structural breaks

Nicholas G. Polson, Rosa L. Matzkin, J. MacKinnon, 2020 .

Sparse Regularization in Marketing and Economics

Nicholas G. Polson, Guanhao Feng, Yuexi Wang, 2017, 1709.00379.

Data Augmentation for Bayesian Deep Learning

Nicholas G. Polson, Vadim O. Sokolov, YueXing Wang, 2019, Bayesian Analysis.

Testing the parametric form of the volatility in continuous time diffusion models - a stochastic process approach

Nicholas G. Polson, Rosa L. Matzkin, J. MacKinnon, 2019 .

Deep Partial Least Squares for Empirical Asset Pricing

Nicholas G. Polson, M. Dixon, Kemen Goicoechea, 2022, SSRN Electronic Journal.

Sampling from log-concave distributions

Nicholas G. Polson, A. Frieze, R. Kannan, 1994 .

Quantile Filtering and Learning

Nicholas G. Polson, Michael S. Johannes, James Yae, 2009 .

Adaptive consistent unit root tests based on autoregressive threshold model

Nicholas G. Polson, Rosa L. Matzkin, J. MacKinnon, 2019 .

Horseshoe Regularization for Machine Learning in Complex and Deep Models

Nicholas G. Polson, A. Bhadra, J. Datta, 2019, 1904.10939.

Deep Learning in Characteristics-Sorted Factor Models

Nicholas G. Polson, Guanhao Feng, Jianeng Xu, 2018, 1805.01104.

Data Augmentation for Bayesian Deep Learning

Nicholas G. Polson, Vadim O. Sokolov, YueXing Wang, 2019, Bayesian Analysis.

Inverse Probability Weighting: from Survey Sampling to Evidence Estimation

Nicholas G. Polson, J. Datta, 2022, 2204.14121.

Feature Selection for Personalized Policy Analysis

Nicholas G. Polson, Vadim O. Sokolov, Maria Nareklishvili, 2022, 2301.00251.

Deep Learning Factor Alpha ∗

Nicholas G. Polson, Guanhao Feng, Jianeng Xu, 2018 .

Deep learning: Computational aspects pdf

Vadim Sokolov, Nicholas Polson, Nicholas G. Polson, 2018, WIREs Computational Statistics.

Deep Learning: A Bayesian Perspective pdf

Vadim Sokolov, Nicholas Polson, Nicholas G. Polson, 2017, ArXiv.

Sparse Bayes estimation in non-Gaussian models via data augmentation

James G. Scott, Nicholas G. Polson, 2011 .

Data augmentation for non-Gaussian regression models using variance-mean mixtures

James G. Scott, Nicholas G. Polson, 2011, 1103.5407.

Simulation-based-Estimation in Portfolio Selection

Nicholas G. Polson, E. Jacquier, Eric Jacquier, 2009 .

Merging Two Cultures: Deep and Statistical Learning pdf

Vadim Sokolov, Nicholas G. Polson, Anindya Bhadra, 2021, WIREs Computational Statistics.

Default Bayesian analysis for multi-way tables: a data-augmentation approach

James G. Scott, Nicholas G. Polson, 2011, 1109.4180.

Bayesian Particle Tracking of Traffic Flows

Nicholas Polson, Nicholas G. Polson, Vadim Sokolov, 2014, IEEE Transactions on Intelligent Transportation Systems.

Deep learning for short-term traffic flow prediction

Nicholas G. Polson, Vadim Sokolov, Nicholas Polson, 2016, 1604.04527.

Chess AI: Competing Paradigms for Machine Intelligence pdf

Nicholas G. Polson, Shiva Maharaj, Nick Polson, 2021, Entropy.

Optimal portfolio choice and stochastic volatility

Nicholas G. Polson, B. Jorgensen, A. Gron, 2012 .

Accelerated Life Tests That Maximize Shannon Information

Nicholas G. Polson, I. Verdinelli, N. Singpurwalla, 1988 .

A utility based approach to information for stochastic differential equations

Nicholas G. Polson, G. Roberts, 1993 .

Volatility Timing and Portfolio Returns

Nicholas G. Polson, Michael S. Johannes, M. Johannes, 2000 .

Sequential Inference for Nonlinear Models using Slice Variables

Nicholas G. Polson, Michael S. Johannes, James Yae, 2009 .

MCMC Methods for Continuous-Time Financial Econometrics

Nicholas G. Polson, Michael S. Johannes, M. Johannes, 2003 .

Analyzing Risky Choices: Q-learning for Deal-No-Deal

Nicholas G. Polson, Laszlo Korsos, 2011, 1110.0883.

Feature Selection for Personalized Policy Analysis

Nicholas G. Polson, Vadim O. Sokolov, Maria Nareklishvili, 2022, 2301.00251.

Generative Causal Inference

Nicholas G. Polson, Vadim O. Sokolov, Maria Nareklishvili, 2023, 2306.16096.

Sampling from log-concave distributions

Nicholas G. Polson, A. Frieze, R. Kannan, 1994 .

Journal of the American Statistical Association Bayesian Inference for Logistic Models Using Pólya–gamma Latent Variables Bayesian Inference for Logistic Models Using P ´ Olya–gamma Latent Variables

James G. Scott, Nicholas G. Polson, Jesse Windle, 2022 .

Posterior Concentration for Sparse Deep Learning

Veronika Rockova, Nicholas G. Polson, Nicholas Polson, 2018, NeurIPS.

Sequential learning , predictive regressions , and optimal p ortfolio returns PRELIMINARY

Nicholas G. Polson, Michael Johannes, Nicholas Polson, 2009 .

Sampling from log-concave distributions

Nicholas G. Polson, A. Frieze, R. Kannan, 1994 .

Generative AI for Bayesian Computation

Nicholas G. Polson, Vadim O. Sokolov, 2023, 2305.14972.

Deep Learning Partial Least Squares

Vadim Sokolov, Nicholas G. Polson, Jianeng Xu, 2021, 2106.14085.

Feature Selection for Personalized Policy Analysis

Nicholas G. Polson, Vadim O. Sokolov, Maria Nareklishvili, 2022, 2301.00251.

Practical filtering with sequential parameter learning

Nicholas G. Polson, Jonathan R. Stroud, A. U.S., 2008 .

CHAPTER 13 – MCMC Methods for Continuous-Time Financial Econometrics

Nicholas G. Polson, M. Johannes, 2010 .

Bayesian analysis of stochastic volatility models with fat-tails and correlated errors

Nicholas G. Polson, Eric Jacquier, Peter E. Rossi, 2004 .

Correction: Sampling from Log-Concave Distributions

Nicholas G. Polson, A. Frieze, R. Kannan, 1994 .

Horseshoe Regularization for Feature Subset Selection

Nicholas G. Polson, Brandon T. Willard, A. Bhadra, 2017, 1702.07400.

Investing in Leveraged Index Funds

Nicholas G. Polson, Jeffrey Y. Yasumoto, 1999 .

An empirical test for Eurozone contagion using an asset-pricing model with heavy-tailed stochastic volatility

James G. Scott, Nicholas G. Polson, 2011, 1110.5789.

Bayesian Analysis of a Stochastic Volatility Model with Leverage Effect and Fat Tails

Peter E. Rossi, Nicholas G. Polson, Eric Jacquier, 2001 .

Particle learning for general mixtures

Nicholas G. Polson, C. Carvalho, H. Lopes, 2010 .

Particle Learning for Sequential Bayesian Computation

M. J. Bayarri, Nicholas G. Polson, D. Heckerman, 2010 .

Deep partial least squares for instrumental variable regression

Nicholas G. Polson, Vadim O. Sokolov, Maria Nareklishvili, 2022, Applied Stochastic Models in Business and Industry.

Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction

M. J. Bayarri, Nicholas G. Polson, A. Dawid, 2010 .

Maximum Expected Utility via MCMC

Nicholas G. Polson, M. Johannes, Eric Jacquier, 2006 .

Bayesian Inference for Gamma Models

Nicholas G. Polson, Jingyu He, Jianeng Xu, 2021, 2106.01906.

Bayesian Analysis of Stochastic Volatility Models

Peter E. Rossi, Nicholas G. Polson, Eric Jacquier, 1994 .

Bayesian Design for Random Walk Barriers

Nicholas G. Polson, G. Parmigiani, 2009 .

Deep Learning in Characteristics-Sorted Factor Models

Nicholas G. Polson, Guanhao Feng, Jingyu He, 2020, Journal of Financial and Quantitative Analysis.