Mathematical Statistics

Institute of Applied Mathematics and Mechanics

Faculty of Mathematics, Informatics and Mechanics

University of Warsaw

ul. Banacha 2,

02-097 Warszawa

Poland

email: noble (at) mimuw (dot) edu (dot) pl

I was born in Aberdeen, Scotland, in 1966, but left Scotland in 1988. I obtained my Ph.D. from University of California, Irvine in 1992, where I studied stochastic partial differential equations. I then worked as a lecturer in Cork, Ireland for three years, in Scuola Normale Superiore di Pisa for two years (1996, 1997) on a postdoctoral fellowship, followed by Lisbon and then KTH, Stockholm. In 2000, I joined the mathematical statistics group at the University of Linköping, Sweden, where I started taking an interest in Bayesian networks. In 2012, I left Linköping and joined the Institute of Applied Mathematics, University of Warsaw. My research interests include stochastic partial differential equations, structure learning for graphical models, generalised diffusions and their applications.

Short answers for the exercises at the end of each chapter may be found here.

- 2022 John M. Noble ‘Lp Solutions for Stochastic Evolution Equation with Nonlinear Potential’ Studia Mathematica 264 (2) pp 181 - 240
- 2018 John M. Noble ‘Effect of Stochastic Perturbations for Front Propagation in Kolmogorov Petrovskii Piscunov Equations’ Stochastic Processes and their Applications 128 no 10 (2018), pp 3531-3557
- 2015 John M. Noble ‘Time Homogeneous Diffusion with Drift and Killing to Meet a Given Marginal’ Stochastic Processes and their Applications 125 (2015), pp. 1500-1540
- 2013 John M. Noble ‘Time Homogeneous Diffusions with a Given Marginal at a Deterministic Time’ Stochastic Processes and Applications vol. 123 (2013) no. 3 pp 675 - 718
- 2012 Timo J.T. Koski and John M. Noble ‘A Review of Bayesian Networks and Structure Learning’ Mathematica Applicanda vol 40 no 1 pp 51 - 103

- 2009 Timo J.T.Koski and John M. Noble ‘Bayesian Networks: An Introduction’ Wiley

- Bayesian Networks

Lectures: Wednesdays 08.30 - 10.00. Tutorials: Wednesdays 10.15 - 11.45 - Multivariate Statistics

Lectures: Mondays 08.30 - 10.00. Tutorials: Mondays 10.15 - 11.45 - Data Science (for Quantitative Psychology and Economics Ph.D. Programme)

Classes: Mondays 13.30 - 15.00. 8 weeks: 21st November 2022 - 23rd January 2023

- Time Series

Lectures: Mondays 08.30 - 10.00, Tutorials: Mondays 10.15 - 11.45. - Statistics

Tutorial group: Tuesdays 10.15 - 11.45.

Spring semester: March - June 2023,

Mondays 15.30 - 17.00, online.