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International Visiting Professors

The M.Sc. in Stochastics and Data Science features courses modules taught by renowned visiting professors who are internationally recognised leading experts in their respective fields of research. These activities are partially supported by: 

 

Academic year 2023/2024

Monte Carlo methods for statistical inference in Stochastic Modelling for Statistical Applications

On discrete time/space random walks and their diffusion approximations in Stochastic Processes

Spectral analysis of time series in Statistics for Stochastic Processes

Asymptotics in Bayesian nonparametrics in Bayesian statistics

 

Previous academic years

A first introduction to diffusion processes in Stochastic Processes

Simulation and inference in Population Genetics in Stochastic Modelling for Statistical Applications

Spectral analysis of time series in Statistics for Stochastic Processes

Causal machine learning in Statistical Machine Learning

Dependent nonparametric priors via completely random measures in Bayesian statistics

Fundamentals of Markov chain Monte Carlo methods in Stochastic Modelling for Statistical Applications

An introduction to Brownian motion in Stochastic Processes

Dynamical processes in complex networks in Complex networks

Frequency domain and spectral analysis in Statistics for Stochastic Processes

Brownian motion and stochastic integrals in Stochastic Differential Equations

  • Bas Kleijn (University of Amsterdam, Netherlands), Fall 2021 

Frequentist limits from Bayesian statistics in Bayesian statistics

  • Goran Peskir (University of Manchester, UK), Spring 2021 

Diffusion processes and boundary classification in Stochastic Processes

Simulation and inference in Population Genetics in Stochastic Modelling for Statistical Applications

Frequency domain and spectral analysis in Statistics for Stochastic Processes

Bayesian inference in high dimensions in Bayesian statistics

Some path properties of Brownian motion in Stochastic Differential Equations

  • Albert Milani (University of Wisconsin-Milwaukee, USA), Fall 2020

Distribution theory, Fourier and Laplace transforms in Analysis

Probability couplings and Monte Carlo in Stochastic Modelling for Statistical Applications

  • Yosef Rinott (Hebrew University of Jerusalem, Israel), Spring 2020

Spectral theory for times series and asymptotics in Statistics for Stochastic Processes

  • Jozsef Lorinczi (Loughborough University, UK), Spring 2020 (canceled for Covid-19 emergency)

Ornstein-Uhlenbeck processes and exit problems in Stochastic Processes

Heuristic introduction to stochastic differential equations with simulation in Stochastic Differential Equations

Asymptotics for posterior Bayesian inference in Bayesian statistics

  • Yosef Rinott (Hebrew University of Jerusalem, Israel), Spring 2019

Levy Processes in Stochastic Modelling for Statistical Application

Frequency domain and spectral analysis in Statistics for Stochastic Processes

  • Samuel Herrmann (Université de Bourgogne Franche-Comté, France) Spring 2019 

Simulation methods for diffusions in Stochastic Processes

Scalable algorithms in modern Bayesian computation in Bayesian statistics

Topics in branching processes in Stochastic Modelling for Statistical Applications

Markov semigroups and diffusion processes in Stochastic Processes

  • Yosef Rinott (Hebrew University of Jerusalem, Israel), Spring 2018

Spectral theory for times series and asymptotics in Statistics for Stochastic Processes

Dynamical processes in complex networks in Complex networks

Computational methods for Bayesian nonparametrics in Bayesian statistics

Introduction to martingales in Probability Theory

Introduction to stochastic modelling in Population Genetics in Stochastic Modelling for Statistical Applications

Dynamical processes in complex networks in Complex networks

Brownian motion in Stochastic Processes

  • Yosef Rinott (Hebrew University of Jerusalem, Israel), Spring 2017

Spectral theory for times series and asymptotics in Statistics for Stochastic Processes

Random partitions and dependent processes in Bayesian nonparametric statistics

Diffusion processes and conditioned processes in Stochastic Processes

Lévy processes and Poisson random measures in Stochastic Modelling for Statistical Applications

 

Last update: 07/02/2024 16:59
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