BayesOpt: A Bayesian Optimization Library for Nonlinear Optimization, Experimental Design and Bandits

BayesOpt is a library with state-of-the-art Bayesian optimization methods to solve nonlinear optimization, stochastic bandits or sequential experimental design problems. Bayesian optimization is sample efficient by building a posterior distribution to capture the evidence and prior knowledge for the target function. Built in standard C++, the library is extremely efficient while being portable and flexible. It includes a common interface for C, C++, Python, Matlab and Octave

Baryon semileptonic decays: the Mexican contribution

We give a detailed account of the techniques to compute radiative corrections in baryon semileptonic decays developed over the years by Mexican collaborations. We explain how the method works by obtaining an expression for the Dalitz plot of semileptonic decays of polarized baryons including radiative corrections to order ${\mathcal O}(\alpha q/\pi M_1)$, where $q$ is the four-momentum transfer and $M_1$ is the mass of the decaying baryon. From here we compute the totally integrated spin angular asymmetry coefficient of the emitted baryon and compare its value with other results.Comment: 10 pages. Prepared for the Commemorative Volume of the Divison of Particles and Fields of the Mexican Physical Societ

Modelling of methane extraction from abandoned coal mines

Imperial Users onl

Practical Bayesian optimization in the presence of outliers

Inference in the presence of outliers is an important field of research as outliers are ubiquitous and may arise across a variety of problems and domains. Bayesian optimization is method that heavily relies on probabilistic inference. This allows outstanding sample efficiency because the probabilistic machinery provides a memory of the whole optimization process. However, that virtue becomes a disadvantage when the memory is populated with outliers, inducing bias in the estimation. In this paper, we present an empirical evaluation of Bayesian optimization methods in the presence of outliers. The empirical evidence shows that Bayesian optimization with robust regression often produces suboptimal results. We then propose a new algorithm which combines robust regression (a Gaussian process with Student-t likelihood) with outlier diagnostics to classify data points as outliers or inliers. By using an scheduler for the classification of outliers, our method is more efficient and has better convergence over the standard robust regression. Furthermore, we show that even in controlled situations with no expected outliers, our method is able to produce better results.Comment: 10 pages (2 of references), 6 figures, 1 algorith

Latino agricultural entrepreneurship strategies, networks of support, and sustainable rural development : Michigan

This presentation is apart of "The Latino Agricultural Entrepreneurship Project", a research and extension project that studied the diversity of Latino agricultural livelihood strategies--from established farmers to emerging farmers, and those interested in becoming farmers--and the agricultural networks that support farming, to identify the capacities needed to improve access to existing knowledge and financial resources in Iowa, Michigan and Missouri. This presentation focuses on the farmers in Michigan

Modeling the Infrared Intensity of a Large Commercial Aircraft

Measuring the infrared signature of large civilian aircraft has become increasingly important due to the proliferation of man-portable air defense systems (MANPADS) and the increasing threat of their use by terrorists. Because of the range of these shoulder-fired weapons, most aircraft flying over 20,000 feet are safe from the threat; however, aircraft taking-off or landing are extremely vulnerable. A radiometric model was developed to simulate a large commercial aircraft’s infrared intensity during these two critical phases of flight. The radiometric model was largely based on the dimensions of a Boeing 747-400 aircraft. It is capable of simulating elevation angles between -20º and +20º, as well as 360º in azimuth in its projected area analysis of the faceted model. The model utilizes an obscuration matrix to determine which parts of the aircraft are in view by the observer and thus contribute to the aircraft’s intensity. A simple one-bounce reflection matrix was also included to incorporate reflections of hot parts off other body parts, as well as earth- and sky-shine contributions to the overall intensity. Various atmospheric scenarios can be loaded into the model to incorporate atmospheric transmittance and radiance effects in the simulation. Measurements taken at the Air Force Research Laboratory’s Optical Measurement Facility are used to create material matrices which account for angle-dependent emissivity and reflectance. A graphical user interface (GUI) was developed to allow a user to change variables and view the resultant aircraft intensity as a function of elevation and azimuth angles. A graphical output of the faceted model assists in visualizing aircraft hot parts, reflections, and/or obstructed parts to identify significant contributions to the aircraft’s infrared intensity