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

Limitations of Initial Orbit Determination Methods for Low Earth Orbit CubeSats with Short Arc Orbital Passes

This thesis will focus on the performance of angles only initial orbit determi- nation (IOD) methods on observational data of low Earth orbit (LEO) CubeSats. Using data obtained by Lockheed Martin’s Space Object Tracking (SpOT) facil- ity, four methods: Gauss, Double-R, Gooding and Assumed Circular, will use different amounts of orbital arc to determine which methods perform the best in the short arc regime of less than 10 degrees of orbital arc. Once the best method for estimating the orbit is determined, there will be analysis on whether these IOD methods are accurate enough to predict a secondary observation session. Finally non-linear regression will be performed to determine if the error metrics follow a predictable trend based on how much orbital arc is seen by the observer. It was determined that above a certain amount of orbital arc, angles only IOD methods can reliably predict a secondary observation session to facilitate more observations. Below 4 degrees of orbital arc, which is around 60 seconds of ob- serving time for LEO objects, none of the methods were able to reliably predict a secondary observation session. The Assumed Circular method was the best method for observing LEO CubeSats because it forces the IOD solution to be circular, which limits the error in the shape of the orbit as the amount of orbital arc decreases. Finally, many metrics follow an exponential trend when compared to the orbital arc. Thus, the amount of orbital arc seen is a strong predictor for the accuracy of the angles only IOD solutions

Rangeland Production Risk Management in Wyoming

Production Economics,

Rangeland Production Risk Management in Montana

Risk and Uncertainty,

Open Your Heart

https://digitalcommons.library.umaine.edu/mmb-vp/3343/thumbnail.jp

Old Fashioned Love

https://digitalcommons.library.umaine.edu/mmb-vp/4020/thumbnail.jp