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for approximate inference, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. It comprises Jan-Willem van de Meent, who
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Bayes factor hypothesis tests in factorial designs. What are you going to do The envisioned projects will focus on the following activities related to Bayesian inference in factorial designs: Construction
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, probabilistic programming, Bayesian deep learning, causal inference, reinforcement learning, graph neural networks, and geometric deep learning. It comprises Jan-Willem van de Meent, who serves as director, Max
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on the following activities related to Bayesian inference in factorial designs: Construction and elicitation of informed prior distributions; Critical assessment of default prior distributions; Organizing a many
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for differentiating effectful programs such as gradient estimation of probabilistic programs, implicit function differentiation, compositional Bayesian inference techniques); analyzing what is required (e.g., choice
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function differentiation, compositional Bayesian inference techniques); analyzing what is required (e.g., choice of data structures, static analyses and compiler optimizations, parallelism and concurrency