averaging   45

Yao , Vehtari , Simpson , Gelman : Using Stacking to Average Bayesian Predictive Distributions
Bayesian model averaging is flawed in the ℳM-open setting in which the true data-generating process is not one of the candidate models being fit. We take the idea of stacking from the point estimation literature and generalize to the combination of predictive distributions. We extend the utility function to any proper scoring rule and use Pareto smoothed importance sampling to efficiently compute the required leave-one-out posterior distributions. We compare stacking of predictive distributions to several alternatives: stacking of means, Bayesian model averaging (BMA), Pseudo-BMA, and a variant of Pseudo-BMA that is stabilized using the Bayesian bootstrap. Based on simulations and real-data applications, we recommend stacking of predictive distributions, with bootstrapped-Pseudo-BMA as an approximate alternative when computation cost is an issue.
bayesian  ensemble  averaging  stacking  model-selection  cross-validation 
june 2018 by arsyed
Sum of Squares Formula Shortcut
trick for finding averages with only sequential operations
average  averaging  midpoint 
may 2018 by vh_hobo_hobo
To Cite or to Steal? When a Scholarly Project Turns Up in a Gallery
What I was seeing was an announcement for a show by Jason Shulman at Cob Gallery called Photographs of Films. The press and interviews collected on the gallery’s website lauded a conceptual beauty and rigor in his work, but the only thing I could see was a rip-off. “Email for price list.” These images were unmistakably similar to the distinctive work I had been producing for years, and it was not long before friends started writing to let me know.
copyright  art  aggregation  averaging  images  movies  rip-offs  jason-shulman  jason-salavon  kevin-l-ferguson 
may 2017 by jm
You CAN Average Percentiles
John Rauser on this oft-cited dictum of percentile usage in monitoring, and when it's wrong and it's actually possible to reason with averaged percentiles, and when it breaks down.
statistics  percentiles  quantiles  john-rauser  histograms  averaging  mean  p99 
july 2016 by jm
[1111.1855] Fr'echet means of curves for signal averaging and application to ECG data analysis
"Signal averaging is the process that consists in computing a mean shape from a set of noisy signals. In the presence of geometric variability in time in the data, the usual Euclidean mean of the raw data yields a mean pattern that does not reflect the typical shape of the observed signals. In this setting, it is necessary to use alignment techniques for a precise synchronization of the signals, and then to average the aligned data to obtain a consistent mean shape. In this paper, we study the numerical performances of Fr\'echet means of curves which are extensions of the usual Euclidean mean to spaces endowed with non-Euclidean metrics. This yields a new algorithm for signal averaging without a reference template. We apply this approach to the estimation of a mean heart cycle from ECG records."
papers  time-series  signal-processing  frechet-mean  smoothing  averaging 
november 2015 by arsyed

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