Accurate and Efficiently Vectorized Sums and Dot Products in Julia

Version submitted to the Correctness2019 workshopThis paper presents an efficient, vectorized implementation of various summation and dot product algorithms in the Julia programming language. These implementations are available under an open source license in the AccurateArithmetic.jl Julia package.Besides naive algorithms, compensated algorithms are implemented: the Kahan-Babuška-Neumaier summation algorithm, and the Ogita-Rump-Oishi simply compensated summation and dot product algorithms. These algorithms effectively double the working precision, producing much more accurate results while incurring little to no overhead, especially for large input vectors.This paper also tries and builds upon this example to make a case for a more widespread use of Julia in the HPC community. Although the vectorization of compensated algorithms is no particularly simple task, Julia makes it relatively easy and straightforward. It also allows structuring the code in small, composable building blocks, closely matching textbook algorithms yet efficiently compiled

Computational Bayesian Methods Applied to Complex Problems in Bio and Astro Statistics

In this dissertation we apply computational Bayesian methods to three distinct problems. In the first chapter, we address the issue of unrealistic covariance matrices used to estimate collision probabilities. We model covariance matrices with a Bayesian Normal-Inverse-Wishart model, which we fit with Gibbs sampling. In the second chapter, we are interested in determining the sample sizes necessary to achieve a particular interval width and establish non-inferiority in the analysis of prevalences using two fallible tests. To this end, we use a third order asymptotic approximation. In the third chapter, we wish to synthesize evidence across multiple domains in measurements taken longitudinally across time, featuring a substantial amount of structurally missing data, and fit the model with Hamiltonian Monte Carlo in a simulation to analyze how estimates of a parameter of interest change across sample sizes

A Practitioner's Guide to Bayesian Inference in Pharmacometrics using Pumas

This paper provides a comprehensive tutorial for Bayesian practitioners in pharmacometrics using Pumas workflows. We start by giving a brief motivation of Bayesian inference for pharmacometrics highlighting limitations in existing software that Pumas addresses. We then follow by a description of all the steps of a standard Bayesian workflow for pharmacometrics using code snippets and examples. This includes: model definition, prior selection, sampling from the posterior, prior and posterior simulations and predictions, counter-factual simulations and predictions, convergence diagnostics, visual predictive checks, and finally model comparison with cross-validation. Finally, the background and intuition behind many advanced concepts in Bayesian statistics are explained in simple language. This includes many important ideas and precautions that users need to keep in mind when performing Bayesian analysis. Many of the algorithms, codes, and ideas presented in this paper are highly applicable to clinical research and statistical learning at large but we chose to focus our discussions on pharmacometrics in this paper to have a narrower scope in mind and given the nature of Pumas as a software primarily for pharmacometricians

Big Thicket National Preserve: Trails to the Future

This report traces the history of the Big Thicket region and the political process that occurred to establish the Big Thicket National Preserve, identifies the current threats facing the Big Thicket region, and describes a continuum of possible policy solutions that might be applied to the threats facing the Big Thicket

Computational Bayesian Methods Applied to Complex Problems in Bio and Astro Statistics

In this dissertation we apply computational Bayesian methods to three distinct problems. In the first chapter, we address the issue of unrealistic covariance matrices used to estimate collision probabilities. We model covariance matrices with a Bayesian Normal-Inverse-Wishart model, which we fit with Gibbs sampling. In the second chapter, we are interested in determining the sample sizes necessary to achieve a particular interval width and establish non-inferiority in the analysis of prevalences using two fallible tests. To this end, we use a third order asymptotic approximation. In the third chapter, we wish to synthesize evidence across multiple domains in measurements taken longitudinally across time, featuring a substantial amount of structurally missing data, and fit the model with Hamiltonian Monte Carlo in a simulation to analyze how estimates of a parameter of interest change across sample sizes

Computational Bayesian methods applied to complex problems in bio and astro statistics.

In this dissertation we apply computational Bayesian methods to three distinct problems. In the first chapter, we address the issue of unrealistic covariance matrices used to estimate collision probabilities. We model covariance matrices with a Bayesian Normal-Inverse-Wishart model, which we fit with Gibbs sampling. In the second chapter, we are interested in determining the sample sizes necessary to achieve a particular interval width and establish non-inferiority in the analysis of prevalences using two fallible tests. To this end, we use a third order asymptotic approximation. In the third chapter, we wish to synthesize evidence across multiple domains in measurements taken longitudinally across time, featuring a substantial amount of structurally missing data, and fit the model with Hamiltonian Monte Carlo in simulation to analyze how estimates of a parameter of interest change across sample sizes

Charrette de Coope Santa Elena

Nuestra tarea fue explorar alternativas de diseño para el centro comercial Coope-CASEM. Cada grupo desarrollo dos alternativas enfocadas a la conservación de espacios abiertos, maximizar el estacionamiento, mejorar el acceso y la visibilidad, controlar el flujo de tráfico y la cohesividad integral de la propiedad.https://digitalcommons.usf.edu/sustainable_futures/1026/thumbnail.jp

Coope Santa Elena charrette

Design proposal for Coope-CASEM Propuesta de diseño para CASEM-Cooperativa de Santa Elenahttps://digitalcommons.usf.edu/sustainable_futures/1024/thumbnail.jp

Coope Santa Elena charrette

Design proposal for Coope-CASEM Propuesta de diseño para CASEM-Cooperativa de Santa Elenahttps://digitalcommons.usf.edu/sustainable_futures/1023/thumbnail.jp