# Questions tagged [bayesian-optimization]

Bayesian optimization is a family of global optimization methods which use information about previously-computed values of the function to make inference about which function values are plausibly optima. Its applications include computer experiments and hyper-parameter optimization in some machine learning models.

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### Noob : Hyperparameter Bayesian Optimization for Stock-Trading Data? [closed]

My data consists of 5 Parameters ( each 3 - 5 values) => 500 unique Parameter-Combinations. I iterate those Parameter-Combinations over 250 different stocks and get Trading Results ( Profit). I get ...
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### Expected Improvement for Sequential Model-based Global Optimization

I am reading a paper about hyper-parameter optimization (Bergstra et al., 2011), and I have a question about the expected improvement. In the paper, the pseudo-code of a generic SMBO is shown as ...
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### Optimizing over a subset of input variables

Consider the unconstraint optimization problem: $$\arg \min_{𝑋_0} f(X_0, X_\eta)$$ where $X_0$ is the input vector I need to optimize for $𝑋_\eta$ is a nuisance vector which I do not care about. ...
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### How to implement Bayesian Optimization when there is a relationship between search space parameters?

I'm quite new to Bayesian models. I'm trying to implement Bayesian Optimization where the sum of parameters should be less than a constant. Say, the parameters are ...
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### Question of understanding regarding Bayesian Optimization, Gaussian process and acquisition function

I'm trying to understand Bayesian optimization and I struggle a lot with all the involved methods. Hence, I have some short questions: We start with a a-prior function, which is a gaussian process. A ...
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### How to allocate the number of samples depending on the variances

I want to compute $p$ number of independent functions with each giving their result as expectation value, $e_{i=1,2..p}$. The final result is the sum of all expectation values, $S = \sum_{i}^{p}e_{i}$....
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### Is Bayesian approach the correct way to solve this problem?

I have a dataset that has 4 variables and the target is a value between 1 to 5. this data is generated from an api that is black box and I don't know the formula that generates these values. I have ...
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### Reduce search space for hyperopt

I am using the model SVR to create a regression model. This class contains several hyperparameters, and to try to find the best ones according to the several features, I am using the library hyperopt. ...
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### Bayesian estimation difference between inferences is conjugate prior and non informative prior [duplicate]

What are the differences in the inferences using non-informative (specifically Jeffrys) priors as compared to the conjugate prior?