OnWorks favicon

Bayesian Optimization download for Windows

Free download Bayesian Optimization Windows app to run online win Wine in Ubuntu online, Fedora online or Debian online

This is the Windows app named Bayesian Optimization whose latest release can be downloaded as v1.4.0.zip. It can be run online in the free hosting provider OnWorks for workstations.

Download and run online this app named Bayesian Optimization with OnWorks for free.

Follow these instructions in order to run this app:

- 1. Downloaded this application in your PC.

- 2. Enter in our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.

- 3. Upload this application in such filemanager.

- 4. Start any OS OnWorks online emulator from this website, but better Windows online emulator.

- 5. From the OnWorks Windows OS you have just started, goto our file manager https://www.onworks.net/myfiles.php?username=XXXXX with the username that you want.

- 6. Download the application and install it.

- 7. Download Wine from your Linux distributions software repositories. Once installed, you can then double-click the app to run them with Wine. You can also try PlayOnLinux, a fancy interface over Wine that will help you install popular Windows programs and games.

Wine is a way to run Windows software on Linux, but with no Windows required. Wine is an open-source Windows compatibility layer that can run Windows programs directly on any Linux desktop. Essentially, Wine is trying to re-implement enough of Windows from scratch so that it can run all those Windows applications without actually needing Windows.



Bayesian Optimization


This is a constrained global optimization package built upon bayesian inference and gaussian process, that attempts to find the maximum value of an unknown function in as few iterations as possible. This technique is particularly suited for optimization of high cost functions, situations where the balance between exploration and exploitation is important. More detailed information, other advanced features, and tips on usage/implementation can be found in the examples folder. Follow the basic tour notebook to learn how to use the package's most important features. Take a look at the advanced tour notebook to learn how to make the package more flexible, how to deal with categorical parameters, how to use observers, and more. Explore the options exemplifying the balance between exploration and exploitation and how to control it. Explore the domain reduction notebook to learn more about how search can be sped up by dynamically changing parameters' bounds.


  • Bayesian optimization works by constructing a posterior distribution of functions
  • As you iterate over and over, the algorithm balances its needs of exploration and exploitation taking into account what it knows about the target function
  • At each step a Gaussian Process is fitted to the known samples (points previously explored), and the posterior distribution,
  • This process is designed to minimize the number of steps required to find a combination of parameters that are close to the optimal combination
  • Bayesian Optimization is most adequate for situations where sampling the function to be optimized is a very expensive endeavor
  • This is a function optimization package, therefore the first and most important ingredient is, of course, the function to be optimized

Programming Language



Data Visualization, Realtime Processing

This is an application that can also be fetched from https://sourceforge.net/projects/bayesian-optimization.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.