Thema der Dissertation:
Dissecting regional heterogeneity and modeling transcriptional cascades in brain organoids Thema der Disputation:
Markov chain Monte Carlo (MCMC) methods
Dissecting regional heterogeneity and modeling transcriptional cascades in brain organoids Thema der Disputation:
Markov chain Monte Carlo (MCMC) methods
Abstract: Probabilistic models incorporate randomness to predict outcomes of a certain event. In the natural sciences, we typically gather data and would like to make generalizations about the underlying process or phenomenon. Under the assumption that the process is stochastic in nature, this can be accomplished by fitting the data to a probabilistic model. A variety of mathematical approaches exist for fitting data to a given probabilistic model, ranging from maximum likelihood estimation to Bayesian inference approaches. In this talk, I will describe how one class of algorithms, Markov chain Monte Carlo (MCMC) methods, approximates the posterior distribution over a set of parameters for a given probabilistic model. In the second talk, I will explore how the application of an ensemble MCMC sampler to single-cell transcriptomics data can be used to explicitly model gene expression dynamics during cell-state transitions.
Zeit & Ort
23.02.2023 | 14:00
Seminarraum 005
(Fachbereich Mathematik und Informatik, Takustr. 9, 14195 Berlin)