Questions tagged [bayesian-optimization]
For questions related to Bayesian optimization (BO), which is a technique used to model an unknown function (that is expensive to evaluate), based on concepts of a surrogate model (which is usually a Gaussian process, which models the unknown function), Bayesian inference (to update the Gaussian process) and an acquisition function (which guides the Bayesian inference). BO can be used for hyper-parameter optimization.
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questions with no upvoted or accepted answers
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How can I draw a Bayesian network for this problem with birds?
I am working on the following problem to gain an understanding of Bayesian networks and I need help drawing it:
Birds frequently appear in the tree outside of your window in the morning and ...
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Can we use a Gaussian process to approximate the belief distribution at every instant in a POMDP?
Suppose $x_{t+1} \sim \mathbb{P}(\cdot | x_t, a_t)$ denotes the state transition dynamics in a reinforcement learning (RL) problem. Let $y_{t+1} = \mathbb{P}(\cdot | x_{t+1})$ denote the noisy ...
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Is it normal to see oscillations in tested hyperparameters during bayesian optimisation?
I've been trying out bayesian hyperparameter optimisation (with TPE) on a simple CNN applied to the MNIST handwritten digit dataset. I noticed that over iterations of the optimisation loop, the tested ...
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Understanding Bayesian Optimisation graph
I came across the concept of Bayesian Occam Razor in the book Machine Learning: a Probabilistic Perspective. According to the book:
Another way to understand
the Bayesian Occam’s razor effect is ...