# 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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### What is a "surrogate model"?

In the following paragraph from the book Automated Machine Learning: Methods, Systems, Challenges (by Frank Hutter et al.) In this section we first give a brief introduction to Bayesian ...
83 views

### Bayesian hyperparameter optimization, is it worth it?

In the Deep Learning book by Goodfellow et al., section 11.4.5 (p. 438), the following claims can be found: Currently, we cannot unambiguously recommend Bayesian hyperparameter optimization as an ...
122 views

### Are there Python packages for random search hyper-parameter optimisation? [closed]

Which Python packages do you recommend for random search hyperparameter optimization to use? Is there any recent and good one (better than the one in scikit-learn)?
238 views

### 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 ...
67 views

### 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 ...
24 views

### 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 ...