By Ilknur Kaynar Kabul, SAS.

Bayesian optimization is an approach to optimizing objective functions that take a long time (minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of less than 20 dimensions, and tolerates stochastic noise in function evaluations. It builds a surrogate for the objective and quantifies the uncertainty in that surrogate using a Bayesian machine learning technique, Gaussian process regression, and then uses an acquisition function defined from this surrogate to decide where to sample. In this tutorial, we describe how Bayesian optimization works, including Gaussian process regression and three common acquisition functions: expected improvement, entropy search, and knowledge gradient. We then discuss more advanced techniques, including running multiple function evaluations in parallel, multi-fidelity and multi-information source optimization, expensive-to-evaluate constraints, random environmental conditions, multi-task Bayesian optimization, and the inclusion of derivative information. We conclude with a discussion of Bayesian optimization software and future research directions in the field. Within our tutorial material we provide a generalization of expected improvement to noisy evaluations, beyond the noise-free setting where it is more commonly applied. This generalization is justified by a formal decision-theoretic argument, standing in contrast to previous ad hoc modifications. A Tutorial on Bayesian Optimization

1. Advanced Modeling

A Shiny Application for End-to-End Bayesian Decision Network Analysis and Web-Deployment (wiseR)A Shiny application for learning Bayesian Decision Networks from data. This package can be used for probabilistic reasoning (in the observational setti …

Christopher Bishop, a Technical Fellow at Microsoft Research, has released his textbook**Pattern Recognition and Machine Learning** as a free PDF download.

At: Tribes.ai Location: Remote, India / Eastern EuropeWeb: tribes.aiPosition: Sr Data Scientist

“Be the Google of Your Organization: Everybody understands that data is key to Google’s dominance. Companies across every industry are trying to establish themselves as the owners and users of the best data available. Watching this data gold rush, it can be easy to forget about the opportunities much closer to home. Within your company, some people are using data to create and advocate effective strategic arguments. How do you ensure that you are a Google within your company, instead of an Alta Vista?” Matt Ritter ( January 5, 2016 )

For the last four years I’ve been working on a book for programmers who want to learn mathematics. It’s finally done, and you can buy it today.

A snapshot on nonstandard supervised learning problems: taxonomy, relationships and methods

Twin Sort Technique The objective behind the Twin Sort technique is to sort the list of unordered data elements efficiently and to allow efficient and simple arrangement of data elements within the data structure with optimization of comparisons and iterations in the sorting method. This sorting technique effectively terminates the iterations when there is no need of comparison if the elements are all sorted in between the iterations. Unlike Quick sort, Merge sorting technique, this new sorting technique is based on the iterative method of sorting elements within the data structure. So it will be advantageous for optimization of iterations when there is no need for sorting elements. Finally, the Twin Sort technique is more efficient and simple method of arranging elements within a data structure and it is easy to implement when comparing to the other sorting technique. By the introduction of optimization of comparison and iterations, it will never allow the arranging task on the ordered elements. …