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Hybrid Quantum-Classical: Europe’s First Exascale Computer Connects to D-Wave
No full textInterview for EE Time
JUPITER
No full textTalk during the visit of the CDU Landtagsfraktion, incl. MdL Patricia Peill, on 30.10.202
Active polymer behavior in two dimensions: A comparative analysis of tangential and push–pull models
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Introduction to Bayesian Statistical Learning
No full textWhen observing data, the key question is: What I can learn from the observation? Bayesian inference treats all parameters of the model as random variables. The main task is to update their distribution as new data is observed. Hence, quantifying uncertainty of the parameter estimation is always part of the task. In this course we will introduce the basic theoretical concepts of Bayesian Statistics and Bayesian inference. We discuss the computational techniques and their implementations, different types of models as well as model selection procedures. We will exercise on the existing datasets use the PyMC3 framework for practicals.The main topics are:Bayes theoremPrior and Posterior distributionsComputational challenges and techniques: MCMC, variational approachesModels: Mixture Models, Bayesian Neural Networks, Variational Autoencoder, Normalizing FlowsPyMC3 framework for Bayesian computationRunning Bayesian models on a Supercompute
