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Abstract: Bayesian inference provides a methodology for parameter estimation and uncertainty quantification in machine learning and deep learning methods. Variational inference and Markov Chain ...

The Bayesian approach to statistical inference and other data analysis tasks gets its name from Bayes’s theorem (BT). BT specifies that a posterior probability for a hypothesis concerning a data ...

Simulation is an indispensable tool in both engineering and the sciences. In simulation-based modeling, a parametric simulator is adopted as a mechanistic model of a physical system. The problem of ...

The School of Computer Science and Statistics (SCSS) ranks 1st in Ireland, a leading School in Europe and in the top 100 universities in the world according to the QS World University Subject Rankings ...

Inferring parameters of computational models that capture experimental data is a central task in cognitive neuroscience. Bayesian statistical inference methods usually require the ability to evaluate ...

Bayesian Networks are graphical models useful for various applications, including time series prediction and anomaly detection. Bayesian inference offers a robust set of tools for modelling ...