Estimating Shapley Effects in Big-Data Emulation and Regression Settings using Bayesian Additive Regression Trees

A. Horiguchi and M.T. Pratola (2027). Statistica Sinica, vol.37, no.4.

Michael Trosset’s contribution to the Discussion of ‘Statistical exploration of the Manifold Hypoth-esis’ by Whitely et al.

To appear in Journal of the Royal Statistical Society, Series B: Statistical Methodology. DOI 10.1093/jrssb/qkag026.

Convergence guarantees for response prediction for latent structure network time series.

A. Acharyya, F. Sanna Passino, M.W. Trosset, C.E. Priebe.  IEEE Trans-actions on Network Science & Engineering, 13:878–888, 2026.

Sacred and Profane: from the Involutive Theory of MCMC to Helpful Hamiltonian Hacks.

Nathan Glatt-Holtz with A. Holbrook, J. Krometis, C. Mondaini and Ami Sheth.  Book Chapter in Handbook of Markov Chain Monte Carlo (Second Edition), 2026.

On the long-time statistical behavior of smooth solutions of the weakly damped, stochastically-driven KdV equation

Nathan Glatt-Holtz with V. Martinez and G. Richards.  Transactions of the American Mathematical Society, 379.01 (2026): 71-136.

Test-Time Scaling for Diffusion Language Models with Frequency-Aware Remasking

B. Zuo*, Y. Yu*, D. Zhou, Y. Zhu. Proceedings of the Fortieth Annual Conference on Neural Information Processing Systems (NeurIPS), 2026 (student co-first author)

On the Limits of Test-Time Compute: Sequential Reward Filtering for Better Inference

Y. Yu, Q. Di, Q. Gu, D. Zhou. Proceedings of the Forty-Third International Conference on Machine Learning (ICML), 2026 (student first author)

Instance-Dependent Continuous-Time Reinforcement Learning via Maximum Likelihood Estimation                               

R. Zhao*, Y. Yu*, R. Wang, C. Huang, D. Zhou. Proceedings of the Forty-Third International Conference on Machine Learning (ICML), 2026 (student co-first author)

Scaled Block Vecchia Ap-proximation for High-Dimensional Gaussian Process Emulation on GPUs

Pan, Q., Abdulah, S., Abduljabbar, M., Ltaief, H., Herten, A., Bode, M., Pratola, M.T., Fadikar, A., Genton, M.G., Keyes, D.E., and Sun, Y. (2025). (submitted to TPDS).

Bayesian model-data comparison incorporating theoretical uncertainties

S. Jaiswal, D. Furnstahl, U. Heinz, C. Shen and M.T. Pratola (2025). Physics Letters B (to appear).

Bayesian Multi-Model Computer Experiments using Gaussian Processes and Regression Trees

M.T. Pratola and J.C. Yannotty (2025). (Handbook on Uncertainty Quantification, to appear).

Bayesian Model Mixing of Computer Simulators with Applications to Climate

J.C. Yannotty, T.J. Santner, B. Li and M.T. Pratola (2025).  Journal of Machine Learning Research (to appear).

Out-of-sample embedding: projection vs. restricted reconstruction. 

M.W. Trosset, K. Tan, M. Tang, C.E. Priebe

To appear in Journal of Computational & Graphical Statistics.  arXiv:2505.06756. 

Aligning Evaluation with Clinical Priorities: Calibration, Label Shift, and Error Costs

Gerardo Flores, Alyssa Smith, Julia Fukuyama, Ashia Wilson.  NeurIPS, 2025.

A decomposition of a phylogenetically-informed distance into basal and terminal components

Julia Fukuyama. Theoretical Population Biology, 2025.

A sitewise model of natural selection on individual antibodies via a transformer-encoder

Frederick A Matsen, Kevin Sung, Mackenzie M. Johnson, Will Dumm, David Rich, Tyler N Starr, Yun S. Song, Philip Bradley, Julia Fukuyama, Hugh Haddox. . Molecular Biology and Evolution, 2025.

Nucleotide context models outperform protein language models for predicting antibody affinity maturation

Mackenzie M. Johnson, Kevin Sung, Hugh Haddox, Ashni Vora, Tatsuya Araki, Gabriel Victoria, Yun Song, Julia Fukuyama, Frederick Matsen. PLoS Computational Biology, 2025

Thrifty wide-context models of B cell receptor somatic hypermutation

Kevin Sung, Mackenzie M. Johnson, Will Dumm, Noah Simon, Hugh Haddox, Julia Fukuyama, Frederick A Matsen IV.  eLife, 2025.

Inferring Mechanistic Parameters of Somatic Hypermutation Using Neural Networks and Approximate Bayesian Computation

Thayer Fisher, Kevin Sung, Noah Simon, Julia Fukuyama*, Erick Matsen*. Annals of Applied Statistics, 2025.

Determining whether weight status mediates the association between number of cigarettes smoked per day and all-cause mortality among US adults who smoke cigarettes.

Luis Miguel Mestre, Roger S. Zoh, Cydne Perry, Julia Fukuyama, Maria A. Parker. PLoS ONE, 2025.

Statistical inference of random graphs with a surrogate likelihood function.

Wu, D., Xie, F., Journal of Machine Learning Research, in press, 2025+ (student first author).

Higher-order entrywise eigenvector analysis of low-rank random matrices: Bias correction, Edgeworth expansion, and bootstrap.

Xie, F., Zhang, Y., Annals of Statistics, 2025, 53 (4), 1667–1696.

The short memory limit for long time statistics in a stochastic Coleman-Gurtin model of heat conduc-tion.

Nathan Glatt-Holtz with V. Martinez, and H. Nguyen.  Evolution Equations and Control Theory. 2025 May 22:0

Embracing Uncertainty in ‘Small Data’ Problems: Estimating Earthquakes from Historical Anecdotes

Nathan Glatt-Holtz with R. Harris, A. J. Holbrook, J. Krometis, Y. Kurniawan, H. Ringer and J. Whitehead.  Journal of Geophysical Research: Machine Learning and Computation 2.3 (2025)

The Hidden Costs of Virtual Learning: Impact of Online Education on K-12 Student Attrition During COVID-19.

An, H., American Journal of Distance Education, 1–13 (2025). 

.

Sample and Computationally Efficient Continuous-Time Reinforcement Learning with General Function Approximation
R. Zhao*, Y. Yu*, A.Y. Zhu, C. Yang, D. Zhou. Proceedings of the Forty-first Conference on Uncertainty in Artificial Intelligence (UAI), 2025 (student co-first author)

Effective Field Theory Convergence Pattern of Modern Nucleon-Nucleon Potentials

P.J. Millican, J.A. Melendez, R.J. Furnstahl, D.R. Phillips and M.T. Pratola (2024).  Physical Review C, vol.110, pp.044002.

Optimizing the induced correlation in omnibus joint graph embeddings.

K. Pantazis, M. Trosset, W.N. Frost, C.E. Priebe, V. Lyzinski

Submitted for publication.  arXiv:2409.17544, 2024. 

Continuous multidimensional scaling.

M.W. Trosset, C.E. Priebe

Submitted for publication. arXiv:2402.04436, 2024. 

Approximate information tests on statistical submanifolds.

M.W. Trosset, C.E. Priebe

Journal of Machine Learning Research, 25(403):1–27, 2024. 

Discussion on "The central role of the identify-ing assumption in population size estimation" by Serge Aleshin-Guendel, Mauricio Sadinle, and Jon Wakefield

Manrique-Vallier, D. (2024).  Biometrics, 80(1):ujad030, ISSN 0006-341X.

Calibrating dimension reduction hyperparameters in the presence of noise.

Justin Lin and Julia Fukuyama. PLoS Computational Biology, 2024.

Bayesian Sparse Gaussian Mixture Model in High Dimensions.

Yao, D., Xie, F., Xu, Y., Journal of Machine Learning Research, 2024, 26 (21), 1–50.

Spectral Norm Posterior Contraction for Bayesian Sparse Spiked Covariance Matrix Model.

Xie, F., Electronic Journal of Statistics, 2024, 18 (2), 5198–5257.

Bias-Corrected Joint Spectral Embedding for Multilayer Networks with Invariant Subspace: Entrywise Eigenvector Perturbation and Inference.

Xie, F., IEEE Transactions on Information Theory, 2024; 70 (12), 9036–9083.

Entrywise limit theorems for eigenvectors of signal-plus-noise matrix models with weak signals.

Xie, F., Bernoulli, 2024, 30 (1), 388–418.

An Eigenvector-Assisted Estimation Framework for Signal-Plus-Noise Matrix Models.

Xie, F., Wu, D., Biometrika, 2024; 111 (2), 661–676

 Long-term accuracy of numerical approximations of SPDEs with the stochastic Navier-Stokes equations as a paradigm

Nathan Glatt-Holtz with C. Mondaini. IMA Journal of Numerical Analysis, July 2024, drae043.

Parallel MCMC Algorithms: Theoretical Foundations, Algorithm Design, Case Studies

Nathan Glatt-Holtz with A. Holbrook, J. Krometis and C. Mondaini.  Transactions of Mathematics and its Applications, Volume 8, Issue 2, December 2024, tnae004

Hydrodynamic stability in the presence of a stochastic forcing: a case study in convection

Nathan Glatt-Holtz with J. F¨oldes, G. Richards, and J. Whitehead.  Physica D: Nonlinear Phenomena, Volume 465, September 2024, 134196

Unique Ergodicity in Stochastic Electroconvection

Nathan Glatt-Holtz with E. Abdo, and M. Ignatova.  Nonlinear Differential Equations and Applications NoDEA, Volume 31, article number 65, May 2024

 On the surprising effectiveness of a simple matrix exponential derivative approximation, with applica-tion to global SARS-CoV-2

Nathan Glatt-Holtz with Gustavo Didier, Andrew J. Holbrook, Andrew F. Magee, Marc A. Suchard.  Proceedings of the National Academy of Sciences (PNAS), 2024 Vol. 121 No. 3 e2318989121

Sharded Bayesian Additive Regression Trees

H. Luo and M.T. Pratola (2023). submitted (JMLR).

Model Mixing using Bayesian Additive Regression Trees

J.C. Yannotty, T.J. Santner, R.J. Furnstahl and M.T. Pratola.  Technometrics  (2023).

Influential Observations in Bayesian Regression Tree Models

M.T. Pratola, R.E. McCulloch and E.I. George, Journal of Computational and Graphical Statis-tics (2023).

Semisupervised regression in latent structure networks on unknown manifolds.

A. Acharyya, J. Agterberg, M.W. Trosset, Y. Park, C.E. Priebe, Applied Network Sci-ence, Volume 8, Article 75, Nov 2023. 

A convenient correspondence between k-mer-based metagenomic distances and phylogenetically-informed β-diversity measures.

Hongxuan Zhai and Julia Fukuyama. PLoS Computational Biology, 2023.

Multiscale Analysis of Count Data through Topic Alignment.

Julia Fukuyama*, Kris Sankaran*, Laura Symul*. Biostatistics, 2023.

Simultaneous Learning the Dimension and Parameter of a Statistical Model with Big Data

Wang, L., Xie, F., and Xu, Y., Statistics in Biosciences, 2023; 15: 583–607.

Efficient Estimation for Random Dot Product Graphs via a One-step Procedure.

Xie, F. and Xu, Y., Journal of the American Statistical Association: Theory & Methods, 2023; 118 (541): 651–664.

Euclidean Representation of Low-Rank Matrices and Its Geometric Properties.

Xie, F., SIAM Journal on Matrix Analysis and Applications, 2023; 44 (2): 822–866.

On the accept-reject mechanism for Metropolis-Hastings algorithms

Nathan Glatt-Holtz with C. Mondaini and J. Krometis. The Annals of Applied Probability, 33(6B): 5279-5333 (December 2023).

 A Statistical Framework for Domain Shape Estimation in Stokes Flows

Nathan Glatt-Holtz with J.Borggaard and J. Krometis.  Inverse Problems, 39 (2023) 085009.

Bayesian Inference using the Proximal Mapping: Uncertainty Quantification under Varying Dimensionality.

Maoran Xu, Hua Zhou, Yujie Hu and Leo L. Duan,  Journal of the American Statistical Association (2023): 1-24.

Bayesian Inference with the l1-ball Prior: Solving Combinatorial Problems with Exact Zeros.

Maoran Xu, and Leo L. Duan. Journal of the Royal Statistical Society: Series B (2023).

Sparse Additive Gaussian Process Regression

H. Luo, G. Nattino and M.T. Pratola (2022).  Journal of Machine Learning Research, vol.23, pp. 1–34.

Using BART to Perform Pareto Optimization and Quantify its Uncertainties

A. Horiguchi, T.J. Santner, Y. Sun and M.T. Pratola (2022).  Technometrics, vol.64, pp. 564–574 (special issue on Industry 4.0).

Popularity adjusted block models are generalized random dot product graphs.

J. Koo, M. Tang, M.W. Trosset, Journal of Computa-tional & Graphical Statistics, 32(1):131–144, 2022. 

Capture-Recapture for Casualty Estimation and beyond: Recent Advances and Research Directions

Manrique-Vallier D., Ball, P., and Sadinle, M. (2022) in A.L. Carriquiry, J.M. Tanur, and W.F. Eddy (eds.), Statistics in the Public Interest: In Memory of Stephen E. Fienberg , chap. 2, New York: Springer International Publishing, 15–31.

Non-Homogeneity estimation and Universal kriging on the sphere

Bussberg, N., Shields, J., and Huang, C. arXiv:2017.02871.
2022

Local biplots for multidimensional scaling, with application to themicrobiome

Julia Fukuyama. Journal of Computational and Graphical Statistics
2022

A Theoretical Framework of the Scaled Gaussian Stochastic Process in Prediction and Calibration.

Gu, M., Xie, F., and Wang, L., SIAM/ASA Journal on Uncertainty Quantification, 2022; 10 (4): 1965–1982.

Bayesian Sparse Spiked Covariance Model With a Continuous Matrix Shrinkage Prior.

Xie, F., Cape, J., Priebe, C.E., and Xu, Y., Bayesian Analysis, 2022; 17 (4): 1193–1217.

Bayesian Projected Calibration for Computer Models.

Xie, F. and Xu, Y., Journal of the American Statistical Association: Theory & Methods, 2022; 116 (536): 1965–1982.

Influential Observations in Bayesian Regression Tree Models

M.T. Pratola, R.E. McCulloch and E.I. George (2021).  In proceedings of the NeurIPS workshop “Your Model is Wrong: Robustness and misspecification in probabilistic modeling.”

The Taxicab Sampler: MCMC for Discrete Spaces with Application to Tree Models

V. Geels, M.T. Pratola and R. Herbei, Journal of Statistical Computation and Simulation (2021).

Get on the BAND Wagon: A Bayesian Framework for Quantifying Model Uncertainties in Nuclear Dynamics

D.R. Phillips, R.J. Furnstahl, U. Heinz, T. Maiti, W. Nazarewicz, F.M. Nunes, M. Plumlee, M.T. Pratola, S. Pratt, F.G. Viens and S.M. Wild (2021).   Journal of Physics G: Nuclear and Particle Physics, vol.48, pp.072001.

Popularity Adjusted Block Models are Generalized Random Dot Product

J. Koo, M. Tang, M.W. Trosset
arXiv:2109.01040, 2021. Submitted for publication.

Bayesian Sparse Spiked Covariance Model With a Continuous Matrix Shrinkage Prior

Xie, F., Xu, Y., Priebe, C.E., and Cape, J.  Bayesian Analysis, accepted for publication
2021

Simultaneous Learning the Dimension and Parameter of a Statistical Model with Big Data

Wang, L., Xie, F., and Xu, Y.  Statistics in Biosciences, accepted for publication
2021

Mixing Rates for Hamiltonian Monte Carlo Algorithms in Finite and Infinite Dimensions

Nathan Glatt-Holtz with C. Mondaini.   Stochastics and Partial Differential Equations: Analysis and Computations, (2021): 1-74.

Sensitivity of steady states in a degenerately-damped stochastic Lorenz system

Nathan Glatt-Holtz with J. F¨oldes, D. Herzog.  Stochastics and Dynamics, Vol. 21, No. 08, 2150055 (2021).

Methodological reconstruction of historical seismic events from anecdotal accounts of destructive tsunamis: a case study for the great 1852 Banda arc mega-thrust earthquake and tsunami

Nathan Glatt-Holtz with H. Ringer, J. Whitehead, J. Krometis, R. Harris, S. Giddens, C. Ashcraft, G. Carver, A. Robertson, M. Harward, J. Fullwood, K. Lightheart, R. Hilton, A. Avery, C. Kesler, M. Morrise, M. Klein).  JGR: Solid Earth, Volume 126, Issue 4, April 2021.

Susan Friedlander’s Contributions in Mathematical Fluid Dynamics

Nathan Glatt-Holtz with A. Cheskidov, N. Glatt-Holtz, N. Pavlovic, R. Shvydkoy, and V. Vicol.  Notices of the American Mathematical Society, (2021): 68(3), pp.331-343.

Invariant measures for the stochastic one-dimensional compressible Navier-Stokes equations

Nathan Glatt-Holtz with M. Coti Zelati, N. Glatt-Holtz, and K. Trivisa.  Applied Mathematics & Optimization, 83, pages 1487–1522 (2021).

A Study on Evidence-based Policy: Debating Points and Its Applicability to the Korean Context.

Han, S., & An, H. (2021). Korean Policy Studies Review, 30(1), 289–314.

On Levy's Brownian motion and white noise space on the circle

Huang, C., and Li, A. Statistics and Probability Letters, 171.
2021

Assessing Variable Activity for Bayesian Regression Trees

A. Horiguchi, M.T. Pratola and T.J. Santner (2020).  Reliability Engineering and System Safety (Special Issue on Sensi-tivity Analysis of Model Outputs), vol.207, pp.107391.

Designing Optimal Experiments: An Application to Proton Compton Scattering

J.A. Melendez, R.J. Furnstahl, H.W. Griebhammer, J.A. McGovern, D.R. Phillips and M.T. Pratola (2020).  European Physical Journal A, vol.57, article 81.

Rehabilitating Isomap: Euclidean representation of geodesic structure

M.W. Trosset, G. Buyukbas
arXiv:2006.10858
2020.  Submitted for publication.

Learning 1-dimensional submanifolds for subsequent inference on random dot product graphs

M.W. Trosset, M. Gao, M. Tang, C.E. Priebe
arXiv:2004.07348
2020. Submitted for publication.

Algorithm 1007: QNSTOP-quasi-Newton algorithm for stochastic

B.D. Amos, D.R. Easterling, L.T. Watson, W.I. Thacker, B.S. Castle, M.W. Trosset
ACM Transactions on Mathematical Software, 46(2):1-20, May 2020.

Book Review: Capture-Recapture Methods for the Social and Medical Sciences–Bohning, Van Der Heijden and Bunge, Eds.

Manrique-Vallier, D (2020). The American Statistician, 74(2):207–208.

Leveraging big data to understand the interaction of task and language during monologic spoken discourse in speakers with and without aphasia

Brielle Stark and Julia Fukuyama. Language, Cognition, and Neuroscience
2020

Lack of evidence for a substantial rate of templated mutagenesis in B cell diversification

Julia Fukuyama, Branden Olson, Frederick Matsen. Journal of Immunology
2020

Bayesian Projected Calibration for Computer Models

Xie, F. and Xu, Y.  Journal of the American Statistical Association: Theory & Methods, in press
2020

Optimal Bayesian Estimation for Random Dot Product Graphs

Xie, F. and Xu, Y.  Biometrika, 2020; 107 (4), 875-889.

Adaptive Bayesian Nonparametric Regression using a Kernel Mixtures of Local Polynomials with Application to Partial Linear Models

Xie, F. and Xu, Y.  Bayesian Analysis, 2020; 15(1): 159-186.

Bandyopadhyay, D.,BAREB: A Bayesian repulsive biclustering model for periodontal data.

Li, Y., Xu, Y., Xie, F., Statistics in Medicine, 2020; 39 (16): 2139–2151.

Bayesian Repulsive Gaussian Mixture Model.

Xie, F. and Xu, Y., Journal of the American Statistical Association: Theory & Methods, 2020; 115(529): 187–203. (Winner of the O-Bayes 2017 Young Investigator Travel Award)

A Bayesian Approach to Estimating Background Flows from a Passive Scalar

Nathan Glatt-Holtz with J. Borggaard and J. Krometis.  SIAM/ASA Journal on Uncertainty Quantification, 8(3):1036 1060, 2020.

On Bayesian Consistency for Flows Observed Through a Passive Scalar

Nathan Glatt-Holtz with J.Borggaard and J. Krometis.  The Annals of Applied Probability, 30(4) (2020): 1762-1783.

The Generalized Langevin Equation with power-law memory in a non-linear potential well

Nathan Glatt-Holtz with D. Herzog, S. McKinley, H. Nguyen.  Nonlinearity, (2020) Volume 33, Number 6.

Quantifying Correlated Truncation Errors in Effective Field Theory

J.A. Melendez, R.J. Furnstahl, D.R. Phillips, M.T. Pratola and S. Wesolowski (2019). Physical Review C, vol.100, pp.044001.

Near-Optimal Design

H.T. Nguyen, M.T. Pratola and P.F. Craigmile (2019). revised.

Building Quantitative Structure-Activity Relationship Using Bayesian Additive Regression Trees

F. Dai, V. Svetnik, A. Liaw, M.T. Pratola and R. Sheridan (2019). Journal of Chemical Information and Modeling, vol.59, pp.2642–2655.

Continuous-time Birth-Death MCMC for Bayesian Regression Tree Models

R. Mohammadi, M.T. Pratola and M. Kaptein (2019).  Journal of Machine Learning Research, vol.21, pp.1–26.

Optimal Design Emulators: a Point Process Approach

M.T. Pratola, C.D. Lin and P.F. Craigmile (2019).  arXiv:1804.02089, revised.

Approximate information tests on statistical submanifolds

M.W. Trosset, C.E. Priebe
arXiv:1903.08656
2019.  Accepted for publication in Journal of Machine Learning Research.

Identifiability of two-component skew normal mixtures with one known

S. Jain, M. Levine, P. Radivojac, M.W. Trosset
Scandinavian Journal of Statistics, 46(4):955-986, December 2019.

Quasi-Newton stochastic optimization algorithm for parameter estimation of a stochastic model of the budding yeast cell cycle.

M. Chen, B.D. Amos, L.T. Watson, J.J. Tyson, Y. Cao, C.A. Shaffer, M.W. Trosset, C. Oguz, G. Kakoti
IEEE/ACM Transactions on Computational Biology & Bioinformatics, 16(1):301–311, 2019. 

Quasi-Newton stochastic optimization algorithm for parameter estimation of a stochastic model of the budding yeast cell cycle

M. Chen, B.D. Amos, L.T. Watson, J.J. Tyson, Y. Cao, C.A. Shaffer, M.W. Trosset, C. Oguz, G. Kakoti.  IEEE/ ACM Transactions on Computational Biology f3 Bioinformatics, 16(1):301-311, 2019.

Low-Risk Population Size Estimates in the Presence of Capture Heterogeneity

Johndrow, J.E., Lum, K., and Manrique-Vallier, D. (2019).  Biometrika, 106(1):197–210, ISSN 0006-3444.

Reality and Risk: A Refutation of S. Rend’on’s Analysis of the Peruvian Truth and Reconciliation Commission’s Conflict Mortality Study

Manrique-Vallier, D. and Ball, P. (2019).  Research & Politics, 6(1), ISSN 2053-1680.

Emphasis on the deep or shallow parts of the tree provides a new characterization of phylogenetic distances

Julia Fukuyama, Genome Biology
2019

Adaptive gPCA: A method for structured dimensionality reduction

Julia Fukuyama. Annals of Applied Statistics
2019

Rates of Contraction with Respect to L2-distance for Bayesian Nonpara-metric Regression.

Xie, F., Jin, W., and Xu, Y., Electronic Journal of Statistics, 2019, Vol. 13, No. 2, 3485–3512.

Data Assimilation in Large-Prandtl Rayleigh-Benard Convection from Thermal Measurements

Nathan Glatt-Holtz with A. Farhat, N. Glatt-Holtz, V. Martinez, S. McQuarrie, J. Whitehead.  SIAM Journal on Applied Dynamical Systems, (2019) (1), 510–540.

On the convergence of stationary solutions in the Smoluchowski-Kramers approximation of infinite dimensional systems

Nathan Glatt-Holtz with S. Cerrai.  Journal of Functional Analysis, (2019) 108421.

GPU-Accelerated Particle Methods for Evaluation of Sparse Observations for PDE-Constrained Inverse Problems

Nathan Glatt-Holtz with J.Borggaard and J. Krometis.  Journal of Computational Physics, 391 (2019): 142-154.

 Large Prandtl Number Asymptotics in Randomly Forced Turbulent Convection

Nathan Glatt-Holtz with J. F¨oldes and G. Richards.  Nonlinear Differential Equations and Applications (NoDEA), (2019) 26: 43.

Exploring the Applicability of Regression Discontinuity in the Fields of Public Administration and Public Policy.

Han, S., & An, H., Modern Society and Public Administration, 29(1), 1–33 (2019). 

Heteroscedastic BART Via Multiplicative Regression Trees

M.T. Pratola, H.A. Chipman, E.I. George and R.E. McCulloch, Journal of Computational and Graphi-cal Statistics (2018). 

Bayesian Calibration of Multistate Stochastic Simulators

M.T. Pratola and O. Chkrebtii (2018).  Statistica Sinica, vol.28, pp.693-720 (special issue on Computer Experiments).

Bayesian Nonparametric Generation of Fully Synthetic Multivariate Categorical Data in the Presence of Structural Zeros

Manrique-Vallier, D. and Hu, J. (2018).  Journal of the Royal Statistical Society: Series A (statistics in Society).

BayCount: A Bayesian Decomposition Method for Inferring Tumor Heterogeneity using RNA-Seq Counts.

Xie, F., Zhou, M., and Xu, Y., Annals of Applied Statistics, 2018, Vol. 12, No. 3, 1605–1627.

Scaling and Saturation in Infinite-Dimensional Control Problems with Applications to Stochastic Partial Differential Equations

Nathan Glatt-Holtz with D. Herzog, J. C. Mattingly.  Annals of PDE, (2018) 4: 16.

Design and Analysis of Experiments on Non-Convex Regions

M.T. Pratola, O. Harari, D. Bingham and G.E. Flowers (2017). Technometrics, vol.59, pp.36-47.

Efficient Metropolis-Hastings Proposal Mechanisms for Bayesian Regression Tree Models Rejoinder

M.T. M.T. Pratola, O. Harari, D. Bingham and G.E. Flowers (2017).  Bayesian Analysis, vol.11, pp.949-955.

Fast embedding for JOFC using the raw stress criterion

V. Lyzinski, Y. Park, C.E. Priebe, M.W. Trosset
arXiv:1502.03391, 2015.  Journal of Com­putational f3 Graphical Statistics, 26(4):786-802, 2017.

Bayesian Simultaneous Edit and Imputation for Multivariate Categorical Data

Manrique-Vallier, D. and Reiter, J.P. (2017). Journal of the American Statistical Association, 112(520):1708–1719.

Asymptotic Analysis for Randomly Forced MHD

Nathan Glatt-Holtz with J. F¨oldes, S. Friedlander, G. Richards.  SIAM Journal on Mathematical Analysis (SIMA), 49(6), 4440–4469, 2017.

Time Discrete Approximation of Weak Solutions for Stochastic Equations of Geophysical Fluid Dynamics and Applications

Nathan Glatt-Holtz with R. Temam and C. Wang.  Chinese Annals of Mathematics, Ser. B 38 2017, no.2, 425–472.

Efficient Metropolis-Hastings Proposal Mechanisms for Bayesian Regression Tree Models (with discussion)

M.T. Pratola (2016). Bayesian Analysis, vol.11, pp.885-911.

Bayesian Additive Regression Tree Calibration of Complex High-Dimensional Computer Models

M.T. Pratola and D. Higdon (2016).  Technometrics, vol.58, pp166-179.

Bayesian Population Size Estimation Using Dirichlet Process Mixtures

Manrique-Vallier, D. (2016).  Biometrics, 72(4):1246–1254, ISSN 1541-0420.

On Unique Ergodicity in Nonlinear Stochastic Partial Differential Equations

Nathan Glatt-Holtz with J. C. Mattingly, G. Richards.  Journal of Statistical Physics, 1–32, 2016.

Ergodicity in Randomly Forced Rayleigh-Benard Convection

Nathan Glatt-Holtz with J. Foldes, G. Richards, J.P. White-head.  Nonlinearity, Nov 2016, Volume 29, Number 11.

Invariant measures for passive scalars in the small noise inviscid limit

Nathan Glatt-Holtz with J. Bedrossian and M. Coti Zelati.  Communications in Mathematical Physics, Nov 2016, Volume 348, Issue 1, pp 101–127.

Inviscid Limits for a Stochastically Forced Shell Model of Turbulent Flow

Nathan Glatt-Holtz with S. Friedlander, V. Vicol.  Annales de l’Institut Henri Poincar´e, Probabilit´es et Statistiques, Volume 52, Number 3 (2016).

LARF: Instrumental Variable Estimation of Causal Effects through Local Average Response Functions.

An, Weihua and Xuefu Wang. Journal of Statistical Software 71(1): 1-13.
2016

Fast embedding for JOFC using the raw stress criterion.

V. Lyzinski, Y. Park, C.E. Priebe, M.W. Trosset
arXiv:1502.03391, 2015. Journal of Com-putational & Graphical Statistics, 26(4):786–802, 2017. 

A Non-parametric, Multiple Imputation-Based Method for the Retrospective Integration of Data Sets

Carrig, M., Ranby, K. W., Manrique-Vallier, D., Reiter, J. and Hoyle, R, Multivariate Behavioral Research 50(4), 383-397
2015

Ergodic and Mixing Properties of The Boussinesq Equations with a Degenerate Random Forcing

Nathan Glatt-Holtz with J. Foldes, G. Richards and E Thomann.  Journal of Functional Analysis, Volume 269, Issue 8, 15 October 2015, Pages 2427-2504.

On Inviscid Limits for the Stochastic Navier-Stokes Equations and Related Models

Nathan Glatt-Holtz with V. Sverak, V. Vicol.  Archive for Rational Mechanics and Analysis, Volume 217, Issue 2 (2015), pp 619-649.

Examining the literature on “Networks in space and in time”: An introduction

De Benedictis, L., Vitale, M.P., & Wasserman, S. Network Science. 3, 1–17.
2015

Parallel deterministic and stochastic global minimization of functions with very many minima

D.R. Easterling, L.T. Watson, M.L. Madigan, B.S. Castle, M.W. Trosset
Com­putational Optimization and Applications, 57:469-492, 2014.

Parallel Bayesian Additive Regression Trees

M.T. Pratola, R. McCulloch, J. Gattiker, H. Chipman and D. Higdon (2014).  Journal of Computational and Graphical Statistics, vol.23, pp.830-852

Parallel deterministic and stochastic global minimization of functions with very many minima.

D.R. Easterling, L.T. Watson, M.L. Madigan, B.S. Castle, M.W. Trosset
Computational Optimization and Applications, 57:469–492, 2014.

Longitudinal Mixed Membership Trajectory Models for Disability Survey Data

Manrique-Vallier, D., Annals of Applied Statistics 8, 2268--2291.
2014

Bayesian Multiple Imputation for Large-Scale Categorical Data with Structural Zeros

Manrique-Vallier, D. and Reiter, J.P. (2014b).  Survey Methodology , 40(1):125–134.

Bayesian Estimation of Discrete Multivariate Latent Structure Models with Structural Zeros

Manrique-Vallier, D. and Reiter, J. (2014). Journal of Computational and Graphical Statistics 23, 1061--1079.

Mixed Membership Trajectory Models

Manrique-Vallier, D. (2014b). E.M. Airoldi, D.M. Blei, E.A. Erosheva, and S.E. Fienberg (eds.), Handbook of Mixed Mem-bership Models and Their Applications, Chapman & Hall/CRC, 173–187.

A Mixed Membership Approach to Political Ideology

Gross, J.H. and Manrique-Vallier, D. (2014).  E.M. Airoldi, D.M. Blei, E.A. Erosheva, and S.E. Fienberg (eds.), Handbook of Mixed Membership Models and Their Applications, Handbooks of Modern Statistical Methods, Chapman & Hall/CRC, 119–138.

Martingale and Pathwise Solutions to the Stochastic Zakharov-Kuznetsov Equation with Multiplicative Noise

Nathan Glatt-Holtz with R. Temam and C. Wang.  Discrete and Continuous Dynamical Systems - Series B, 19 (2014), no. 4, 1047-1085.

Existence and Regularity of Invariant Measures for the Three Dimensional Stochastic Primitive Equations

Nathan Glatt-Holtz with I. Kukavica, V. Vicol and M. Ziane.  Journal of Mathematical Physics, 55, 051504 (2014).

Unique Ergodicity for Fractionally Dissipated, Stochastically Forced 2D Euler Equations

Nathan Glatt-Holtz with P. Constantin and V. Vicol.  Communications in Mathematical Physics, September 2014, Volume 330, Issue 2, pp 819-857.

Local and global existence of smooth solutions for the stochastic Euler equations with multiplicative noise.

Nathan Glatt-Holtz with V. Vicol.  Annals of Probability, Volume 42, Number 1 (2014), 1-430.

Fast Sequential Computer Model Calibration of Large Non-Stationary Spatial-Temporal Processes

M. T. Pratola, S. Sain, M. Wiltberger, J. Rigler and D. Bingham (2013).  Technometrics, vol.55, pp.232-242

Computer Model Calibration using the Ensemble Kalman Filter

D. Higdon, M.T. Pratola, J. Gattiker, E. Lawrence, S. Habib, K. Heitmann, S. Price, C. Jackson and M. Tobis (2013).  Technometrics, vol.55, pp.488-500.

Adjusting process count on demand for petascale global optimization.

M. Sosonkina, L.T. Watson, N.R. Radcliffe, R.T. Haftka, M.W. Trosset
Parallel Computing, 39(1):21–35, 2013.

Estimating Identification Disclosure Risk Using Mixed Membership Models

Manrique-Vallier, D. and Reiter, J. (2012).  Journal of the American Statistical Association 107, 1385-1394.

Poisson tests of declustered catalogs

Brad Luen and Philip B. Stark. Geophysical Journal International, 189, 691{700.
2012

Invariant Measures for Dissipative Dynamical Systems: Abstract Results and Applications

Nathan Glatt-Holtz with M.D. Chekroun.  Communications in Mathematical Physics, vol 316, 723-761 (2012).

Global Existence and Regularity for the 3D Stochastic Primitive Equations of the Ocean and Atmosphere with Multiplicative White Noise

Nathan Glatt-Holtz with A. Debussche, R. Temam and M. Ziane.  Nonlinearity, vol 25 (2012) 2093-2118.

Social network research: The foundation of network science. Handbook of Research Methods in Psychology.

Wasserman, S., & Robins, G. New York: APA Press.
2012

Network analysis: A definitional guide to important concepts. In Little, T.D. (ed.) Oxford Handbook of Quantitative Methods.

Green, H., & Wasserman, S. New York: Oxford University Press.
2012

Local Martingale and Pathwise Solutions for an Abstract Fluids Model

Nathan Glatt-Holtz with A. Debussche and R. Temam.  Physica D, vol 240, Issues 14-15, 15 July 2011, pp. 1123-1144.

 Parameter Estimation for the Stochastically Perturbed Navier-Stokes Equations

Nathan Glatt-Holtz with I. Cialenco.  Stochastic Processes and their Applications, vol 121, pp. 701-724, 2011.

Pathwise Solutions of the 2-D Stochastic Primitive Equations

Nathan Glatt-Holtz with R. Temam.  Applied Mathematics and Optimization, Vol 63, No 3, 401-433

Cauchy Convergence Schemes for Some Nonlinear Partial Differential Equations

Nathan Glatt-Holtz with R. Temam.  Applicable Analysis, Special Issue in honor of V. Solonnikov, Vol 90, No 1, 2011, 85 - 102.

Asymptotics of the Coleman-Gurtin model

Nathan Glatt-Holtz with M.D. Chekroun, F. Di Plinio, and V. Pata.  Discrete and Continuous Dynamical Systems-Series S, (Vol. 4, No. 2) April 2011.

Introduction: Special issue of Statistical Analysis and Data Mining on Networks. Statistical Analysis and Data Mining.

Steinley, D., & Wasserman, S. 4, 459–460.
2011

Developments in Computer Model Calibration

M. T. Pratola (2010).  Ph.D. Thesis, Dept. of Statistics and Actuarial Science, Simon Fraser University

Dimensionality reduction on the Cartesian product of embeddings of multiple dissimilarity matrices.

Z. Ma, A. Cardinal-Stakenas, Y. Park, M.W. Trosset, C.E. Priebe
Journal of Classification, 27(3):307–321, 2010.

Euclidean and circum-Euclidean distance matrices: characterizations and linear preservers.

C.-K. Li, T. Milligan, M.W. Trosset
Electronic Journal of Linear Algebra, 20:739–752, 2010.

Singular Perturbation Systems with Stochastic Forcing and the Renormalization Group Method

Nathan Glatt-Holtz with M. Ziane.  Discrete and Continuous Dynamical Systems-Series A (2010), Vol 26, No 4.

A Library of Eyes in Go II: Monolithic Eyes

T. Wolf and M. Pratola (2009).  Games of No Chance 3, vol.56, pp.249-268

Molecular embedding via a second-order dissimilarity parameterized approach.

I.G. Grooms, R.M. Lewis, M.W. Trosset
SIAM Journal on Scientific Computing, 31(4):2733–2756, 2009.

Integrated Methodology for Multiple Systems Estimation and Record Linkage Using a Missing Data Formulation

Fienberg, S.E. and Manrique-Vallier, D. (2009). AStA-Advances in Statistical Analysis, 93(1):49–60.

Strong Pathwise Solutions of the Stochastic Navier-Stokes System

Nathan Glatt-Holtz with M. Ziane.  Advances in Differential Equations, 14 (2009), no. 5-6, 567-600.

The Stochastic Primitive Equations in Two Space Dimensions with Multiplicative Noise

Nathan Glatt-Holtz with M. Ziane.  Discrete and Continuous Dynamical Systems-Series B, (2008), no. 4, 801822.

Numerical integration in statistical decision-theoretic methods for robust design optimization.

S.C. Kugele, M.W. Trosset, L.T. Watson

Structural and Multidisciplinary Optimization, 36(5):457–475, November 2008.

Population Size Estimation Using Individual Level Mixture Models 

Manrique-Vallier, D. and Fienberg, S.E. (2008).  Biometrical Journal , 50(6):1051–1063.

Testing earthquake predictions. IMS Lecture Notes–Monograph Series.

Brad Luen and Philip B. Stark. Probability and Statistics: Essays in Honor of David A. Freedman, 302–315 (2008).

Nonmonotonicity of phase transitions in a loss network with controls.

Brad Luen, Kavita Ramanan and Ilze Ziedins, Annals of Applied Probability, 16(3), 1528–1562 (2006). 

Optimizing GoTools’ Search Heuristics using Genetic Algorithms

M. Pratola and T. Wolf (2003). ICGA Journal 26, vol.1, pp.28-49