Recent preprints

J. Windle. Inferring monocotyledon crown root trajectories from limited data. 2023.

J. Windle. Sampling from a Gaussian distribution conditioned on the level set of a piecewise affine, continuous function. arXiv preprint arXiv:2303.12185, 2023.

Papers

J.J. Aguilar, M. Moore, L. Johnson, R.F. Greenhut, E. Rogers, D. Walker, F. O’Neil, J.L. Edwards, J. Thystrup, S. Farrow, J. Windle, P.N. Benfey. Capturing in-field root system dynamics with RootTracker. Plant Physiology, 187(3):1117–1130 (November 2021).

J. Windle and C. Carvalho. A tractable state-space model for symmetric positive-definite matrices. Bayesian Analysis, 9(4): 759-792 (December 2014).

N. Polson, J.G. Scott, and J. Windle. The Bayesian Bridge. Journal of the Royal Statistical Society Series B: Statistical Methodology, 76(4): 713–733 (September 2014).

N. Polson, J.G. Scott, and J. Windle. Bayesian Inference for Logistic Models Using Polya–Gamma Latent Variables. Journal of the American Statistical Association, 108(504): 1339-1349 (December 2013).

Software

J. Windle. rootmodel. Github, 2023. The code (R and Stan) supporting the paper Inferring monocotyledon crown root trajectories from limited data.

J. Windle. gmmfun. Github, 2023. A Python package for fitting distributions via their moment generating function using the generalized method of moments.

J. Windle. ctgauss. Github, 2023. A Python package for sampling from a Gaussian random variable conditioned on a piecewise linear function.

J. Windle, N. Polson, J.G. Scott. BayesLogit, Github, 2013. An R package for sampling from the family of Polya-Gamma distributions.

Fellowships

Fusion Fund Fellowship, 2023

Other products

J. Windle, C.M. Carvalho, J.G. Scott, L. Sun. Efficient Data Augmentation in Dynamic Models for Binary and Count Data. arXiv preprint, arXiv:1308.0774, 2013.

J. Windle. Forecasting High-Dimensional, Time-Varying Variance-Covariance Matrices with High-Frequency Data and Sampling Polya-Gamma Random Variates for Posterior Distributions Derived from Logistic Likelihoods. University of Texas at Austin, 2013, Ph.D. Thesis.