9k views

### Why should I be Bayesian when my model is wrong?

Edits: I have added a simple example: inference of the mean of the $X_i$. I have also slightly clarified why the credible intervals not matching confidence intervals is bad. I, a fairly devout ...
26k views

### Practical hyperparameter optimization: Random vs. grid search

I'm currently going through Bengio's and Bergsta's Random Search for Hyper-Parameter Optimization [1] where the authors claim random search is more efficient than grid search in achieving ...
9k views

### When are genetic algorithms a good choice for optimization?

Genetic algorithms are one form of optimization method. Often stochastic gradient descent and its derivatives are the best choice for function optimization, but genetic algorithms are still sometimes ...
12k views

### How could stochastic gradient descent save time compared to standard gradient descent?

Standard Gradient Descent would compute gradient for the entire training dataset. ...
7k views

### How to run linear regression in a parallel/distributed way for big data setting?

I am working on a very large linear regression problem, with data size so large that they have to be stored on a cluster of machines. It will be way too big to aggregate all the samples into one ...
548 views

### Optimization of stochastic computer models

This is a tough topic for myself to google since having the words optimization and stochastic in a search almost automatically defaults to searches for stochastic optimization. But what I really want ...
704 views

### Which optimization algorithm to use for problems with many local optima and expensive goal function? [duplicate]

I've got an optimization problem with these properties: The goal function is not cheap to compute. It can be evaluated up to around 10^4 times in the optimization. There's a lot of local optima. High ...
5k views

### Markov chain Monte Carlo (MCMC) for Maximum Likelihood Estimation (MLE)

I am reading a 1991 conference paper by Geyer which is linked below. In it he seems to elude to a method that can use MCMC for MLE parameter estimation This excites me since, I have coded BFGS ...
347 views

### Best way to evaluate PDF estimation methods

I wish to test some of my ideas that I think are better than anything that I have seen. I could be wrong but I'd like to test my ideas and vanquish my doubts by more certain observations. What I have ...
3k views

### What are some of the disavantage of bayesian hyper parameter optimization?

I am fairly new to machine learning and statistics but I was wondering why bayesian optimization is not referred more often online when learning machine learning to optimize your algorithm ...
9k views

### hyperparameter tuning in neural networks

I was trying to fine tune a neural network model for a multilabel classification problem. I was reading Jason Brownlee 's article for the same. As per the article, there are a number of parameters to ...
2k views

### Optimizing a “black box” function: Linear Regression or Bayesian Optimization… what's the difference?

Goal: I have a function $f(x,y)=z$ (two variables for illustration only) which I know almost nothing about--it has a compact domain which I can determine, it is non-negative, and bounded above. My ...
614 views

### Regression: find the best degree of polynomial with the best regularization parameter

When trying to predict data using linear regression or classify with logistic regression, with a polynomial, I know how to find the best degree of a polynomial to fits given data when the ...
160 views

### What optimization (maximization/minimization) methods exist contours with lots of kinks?

I am wondering what methods exist out there for optimization on problems where your contour has lots of small sharp kinks. For example, suppose we have a contour where there are lots of lots of spiky, ...
749 views

### Bayesian Hyperparameter Tuning

I would like to implement Bayesian hyperparameter tuning in Python, but I am struggling to find a complete explanation of how it is exactly working. As anyone links, books or papers I could read about ...

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