Chalmers

Physiological Gaussian Process Priors for the Hemodynamics in fMRI Analysis

Speaker: 
​Josef Wilzén (Linköping)

Inference from fMRI data faces the challenge that the hemodynamic system, that relates the underlying neural activity to the observed BOLD fMRI signal, is not known. We propose a new Bayesian model for task fMRI data with the following features: (i) joint estimation of brain activity and the underlying hemodynamics, (ii) the hemodynamics is modeled nonparametrically with a Gaussian process (GP) prior guided by physiological information and (iii) the predicted BOLD is not necessarily generated by a linear time-invariant (LTI) system.

Time of Seminar: 
2018-11-21 13:15
University: 

Assembling stochastic quasi-Newton algorithms using Gaussian processes

Speaker: 
Thomas Schön (Uppsala University)

Abstract: In this talk I will focus on one of our recent developments where we show how the Gaussian process (GP) can be used to solve stochastic optimization problems. Our main motivation for studying these problems is that they arise when we are estimating unknown parameters in nonlinear state space models using sequential Monte Carlo (SMC). The very nature of this problem is such that we can only access the cost function (in this case the likelihood function) and its derivative via noisy observations, since there are no closed-form expressions available.

Time of Seminar: 
2018-11-07 13:15
University: 

Non-parametric methods for learning continuous-time dynamical systems

Speaker: 
Harri Lähdesmäki

Welcome to a seminar with Harri Lähdesmäki (Aalto) on 25 October at 13.15 in room MV:L14 at Dept. Mathematical Sciences (Chalmers tvärgata 3, Gothenburg).

Time of Seminar: 
2018-10-25 13:15
University: 
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