Matthew Bognar
Associate Professor of Instruction
Biography
Research interests
- Educational software
- High performance computing (HPC)
- Parallel computing
- Programming languages: C++, Java, R, OpenMP, Eigen, TNT, Boost, Blitz, Intel MKL, Objective C/C++, Flutter, Swift/SwiftUI
- Stochastic differential equations (SDE's)
Selected awards and honors
- Distinguished Associate Professor of Instruction, CLAS, University of Iowa, Iowa City, Iowa, United States, 2018 - 2020
- Collegiate Teaching Award, CLAS, University of Iowa, Iowa City, Iowa, United States, 2017 - 2018
- Finalist for the Leonard J. Savage Thesis Award, International Society for Bayesian Analysis (ISBA), 2003
- Outstanding Teaching Assistant Award, University of Iowa Council on Teaching, Iowa City, Iowa, 2000
- Allen T. Craig Award, University of Iowa, Department of Statistics and Actuarial Science, 1998
Selected publications
- Stramer, O., Shen, X. & Bognar, M. A. (2017). Bayesian inference for Heston-STAR models. Statistics and Computing, 27(2), 331-348. DOI: 10.1007/s11222-015-9625-y.
- Stramer, O., Bognar, M. A. (2011). Bayesian inference for irreducible diffusion processes using the pseudo-marginal approach. Bayesian Analysis, 6(2), 231-258. DOI: 10.1214/11-ba608.
- Stramer, O., Bognar, M. A. & Schneider, P. (2010). Bayesian inference for discretely sampled Markov processes with closed-form likelihood expansions. Journal of Financial Econometrics, 8(4), 450-480. DOI: 10.1093/jjfinec/nbp027.
- Bognar, M. A. (2008). Bayesian modeling of continuously marked Gibbsian point processes. Computational Statistics, 23(3), 361-379. DOI: 10.1007/s00180-007-0073-9.
- Bognar, M. A. (2006). On Bayesian inference for the K-function. Biometrical Journal, 48, 205-219. DOI: 10.1002/bimj.200410166.
- Bognar, M. A. (2005). Bayesian inference for spatially inhomogeneous pairwise interacting point processes. Computational Statistics and Data Analysis, 49, 1-18. DOI: 10.1016/j.csda.2004.04.008.
- Bognar, M. A., Cowles, K. (2004). Bayesian inference for pairwise interacting point processes. Statistics and Computing, 14, 109--117. DOI: 10.1023/b:stco.0000021409.73461.b9.
Research areas
- Bayesian statistics
- Computational statistics
- Markov chain Monte Carlo
- Spatial and environmental statistics
- Time series analysis