123 research outputs found
approxposterior: Approximate Posterior Distributions in Python
No full textThis package is a Python implementation of Bayesian Active Learning for Posterior Estimation by Kandasamy et al. (2015) and Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions by Wang & Li (2017). These algorithms allows the user to compute approximate posterior probability distributions using computationally expensive forward models by training a Gaussian Process (GP) surrogate for the likelihood evaluation. The algorithms leverage the inherent uncertainty in the GP's predictions to identify high-likelihood regions in parameter space where the GP is uncertain. The algorithms then run the forward model at these points to compute their likelihood and re-trains the GP to maximize the GP's predictive ability while minimizing the number of forward model evaluations. Check out Bayesian Active Learning for Posterior Estimation by Kandasamy et al. (2015) and Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions by Wang & Li (2017) for in-depth descriptions of the respective algorithms.</p
Season 3 Episode 20: Choosing a Children\u27s Camp
No full textHow can parents make sure their children will be safe, well cared for, and properly guided during their time away at camp? Helpful advice is provided by our three guests: James Van Wingerden, executive director of Camp Roger in Rockford, MI; Karen Saupe, summer program director at Camp Mowana in Richland County, OH (and Calvin professor of English); and Calvin senior Jake Vander Plas, counselor at Mt. Hermon Redwood Camp in Mt. Hermon, CA
In Defense of Extreme Openness
No full textIn this short (~4min) talk I discuss a case study of "extreme" open research, where I made my data exploration, my code, and the text of my paper available from day 1 of the project.Presented as a lightning talk at the Python in Astronomy Conference, 201
Demonstrative Evidence and the Use of Algorithms in Jury Trials
No full textWe investigate how the use of bullet comparison algorithms and demonstrative evidence may affect juror perceptions of reliability, credibility, and understanding of expert witnesses and presented evidence. The use of statistical methods in forensic science is motivated by a lack of scientific validity and error rate issues present in many forensic analysis methods. We explore what our study says about how this type of forensic evidence is perceived in the courtroom – where individuals unfamiliar with advanced statistical methods are asked to evaluate results in order to assess guilt. In the course of our initial study, we found that individuals overwhelmingly provided high Likert scale ratings in reliability, credibility, and scientificity regardless of experimental condition. This discovery of scale compression - where responses are limited to a few values on a larger scale, despite experimental manipulations - limits statistical modeling but provides opportunities for new experimental manipulations which may improve future studies in this area.This article is published as Rachel Rogers, Susan VanderPlas, Demonstrative Evidence and the Use of Algorithms in Jury Trials, J. data sci.(2024), 1-19, DOI 10.6339/24-JDS1130. © 2024 The Author(s). Posted with permission of CSAFE.Open access article under the CC BY license
Python data science handbook: essential tools for working with data
No full textFor many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues
supersmoother: Efficient Python Implementation of Friedman's SuperSmoother
No full text<p>The supersmoother package is an efficient pure-Python implementation of Friedman's SuperSmoother algorithm, utilizing numpy for fast numerical computation. See more at http://github.com/jakevdp/supersmoother/</p>
Chimes: September 15, 2000
No full textCalvin changes alcohol policy by Nathan VanderKlippe
CVN woes continue with broken equipment, little support by Beth Heinen
Human egg donation: an ethical debate by Erin Miller
Pauley wins national award by Nathan Bierma
Broene Center receives new director by Beth Heinen
FTC criticizes makers of violent entertainment by Kristin Werkhoven
Women\u27s soccer struggling to find identity by Nathan VanderKlippe
Professors bring international styles to Calvin by Sarah VanKuiken
Zap Mama brings world beat to Calvin by Tim Thompson
The queen is gone: Waffle Bar loses its magic by Jake VanderPlas
New student groups form across campus by Beth Heinenhttps://digitalcommons.calvin.edu/chimes/1446/thumbnail.jp
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