Patterns of Scalable Bayesian Inference

Datasets are growing not just in size but in complexity, creating a demand for rich models and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but scaling Bayesian inference is a challenge. In response to this challenge, there has been considerable recent work based on varying assumptions about model structure, underlying computational resources, and the importance of asymptotic correctness. As a result, there is a zoo of ideas with few clear overarching principles. In this paper, we seek to identify unifying principles, patterns, and intuitions for scaling Bayesian inference. We review existing work on utilizing modern computing resources with both MCMC and variational approximation techniques. From this taxonomy of ideas, we characterize the general principles that have proven successful for designing scalable inference procedures and comment on the path forward

Accelerating MCMC via Parallel Predictive Prefetching

We present a general framework for accelerating a large class of widely used Markov chain Monte Carlo (MCMC) algorithms. Our approach exploits fast, iterative approximations to the target density to speculatively evaluate many potential future steps of the chain in parallel. The approach can accelerate computation of the target distribution of a Bayesian inference problem, without compromising exactness, by exploiting subsets of data. It takes advantage of whatever parallel resources are available, but produces results exactly equivalent to standard serial execution. In the initial burn-in phase of chain evaluation, it achieves speedup over serial evaluation that is close to linear in the number of available cores

Interview with Cecilia Lauby-Ryan, Faculty Emeritus

Oral history interview with Illinois State University Emeritus Professor Cecilia Lauby-Ryan. The interview was conducted on October 11, 2006, by Elaine Graybill, as part of the Illinois State University Oral History Project.https://ir.library.illinoisstate.edu/soh/1004/thumbnail.jp

Analysis of Observed Contamination Through SAGE III's First Year on Orbit

SAGE III is a payload on the International Space Station that conducts measurements of ozone and other atmospheric constituents through the use of a moderate resolution spectrometer with an operating wavelength range of 290 nm to 1550 nm. Because of the optically sensitive nature of the payload, a suite of eight Thermoelectric Quartz Crystal Microbalances (TQCMs) were included to monitor the operating environment. During the rst year of operation, the SAGE III TQCMs were instrumental in detecting several periods of higher contamination and localizing their sources. A clear window made from quartz crystal covers the instrument assembly's aperture. Under nominal operating conditions, this window is only open during science gathering activities. However, if the rates of contamination accumulation are detected to be above the background rate, the window will be kept closed during science gathering to protect the optically sensitive instrument mirror. An analysis of the signal transmissions through the window for the wavelengths of 290 nm to 1550 nm has been conducted to determine any possible degradation of the window and potential in uence on science data collected to date, and established a baseline for future analysis

The characteristics of the breaststroke pullout in elite swimming

Since the rule change permitting the inclusion of one dolphin kick during the underwater breaststroke pullout phase following a swim start or turn, there has been an emergence of several different pullout techniques adopted by elite swimmers. The aim of this study was to characterize the underwater breaststroke pullout technique trends and to assess the effectiveness of each technique as utilized by elite male and female swimmers. The sample included 60 swimmers (n = 26 male, n = 34 female) competing across the 50, 100, and 200 m long-course breaststroke final races from the World Championships 2015, 2017, 2019 and Olympic Games 2016. An above-water camera was used to identify and measure the different phases of the underwater pullout techniques, which was found to be a highly accurate methodological approach (ICC = 0.97). From the 150 trials analyzed, three different pullout techniques were identified: the Fly-Kick First technique, the Combined technique and the Pull-Down First technique. Although the most common underwater pullout technique utilized by elite competitive breaststroke swimmers was the Combined technique (n = 71), followed by the Fly-Kick First technique (n = 65) and the Pull-Down First technique (n = 14), it was observed that technical selection deviates according to gender. This indicates that male and female swimmers should not be coached adhering to the same technical model. This study found no significant difference in terms of performance outcome with respect to each of these techniques, indicating that technique selection should be guided by one's individual preference. It was concluded that the results of this study will serve as an up-to-date resource for coaches and swimmers working with elite breaststroke swimmers and as a useful insight to current underwater pullout trends

Conversion of aldoses to valuable ω-amino alcohols using amine transaminase biocatalysts

The conversion of readily available monosaccharides to high-value amino alcohols using a key biocatalytic step is an attractive strategy for the preparation of these chiral synthons. Here, we report a previously undescribed example of the direct amination of monosaccharides, which exist predominantly in their cyclic form at equilibrium, using amine transaminase biocatalysts, providing access to a panel of amino alcohols in moderate to high conversion and isolated yield. A recently developed high-throughput colorimetric screen, employing o-xylylenediamine, was initially used to identify amine transaminase enzymes displaying this activity towards cyclic sugars and reactions were successfully scaled-up using isopropylamine