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How music choices can affect productivity

How music choices can affect productivity
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Rose Faghih , Saman Khazaei , Bayesian Inference , Hidden Cognitive Performance , Arousal States ,

"A new OSL dose model to account for post-depositional mixing of sedime" by Luke A. Yates, Zach Aandahl et al.

In applications of optically stimulated luminescence (OSL) dating to unconsolidated sediments, the burial age of a sample of grains is estimated using statistical models of the distribution of the experimentally determined equivalent doses of the grains, together with estimates of the environmental dose rate. For grains that have been vertically mixed after deposition (e.g., due to bioturbation), existing dose models may fail to appropriately account for the complexity of the mixing process, thus producing inaccurate age estimates of the original time of deposition of the ‘native’ grains in any particular sample (usually the quantity of most interest). Here we introduce a new dose model, the asymmetric Laplacian mixture model (ALMM), developed for vertically mixed samples with single-grain dose distributions. The approach is based on a continuous statistical mixture that models the displacement of grains in both upward and downward directions. The central dose of the native grains ....

Monte Carlo , Nawarla Gabarnmang , Hamiltonian Monte Carlo , Asymmetric Laplacian Mixture Model , Bayesian Inference , Maximum Likelihood Estimation , Optically Stimulated Luminescence , Ingle Grain Dose Distributions , Ertical Mixing Of Sediments ,

"A LOG-GAUSSIAN COX PROCESS WITH SEQUENTIAL MONTE CARLO FOR LINE NARROW" by Teemu Härkönen, Emma Hannula et al.

We propose a statistical model for narrowing line shapes in spectroscopy that are well approximated as linear combinations of Lorentzian or Voigt functions. We introduce a log-Gaussian Cox process to represent the peak locations thereby providing uncertainty quantification for the line narrowing. Bayesian formulation of the method allows for robust and explicit inclusion of prior information as probability distributions for parameters of the model. Estimation of the signal and its parameters is performed using a sequential Monte Carlo algorithm followed by an optimization step to determine the peak locations. Our method is validated using a simulation study and applied to a mineralogical Raman spectrum. ....

Monte Carlo , Gaussian Cox , Bayesian Inference , Ourier Self Deconvolution , Article Filtering And Smoothing , Leak Detection , Oisson Process , Statistical Signal Processing ,

Dealing with the limitations of our noisy world

MIT Professor Tamara Broderick uses Bayesian inference to quantify uncertainty in an effort to better understand the limits of data analysis techniques. She collaborates with scientists in an array of fields, helping them craft better data analysis for their research. ....

United States , United Kingdom , Johns Hopkins , Tamara Broderick , Institute For Data , Case Western University , University Of California At Berkeley , Laboratory For Information , Department Of Electrical Engineering , Women Technology Program , Cambridge University , Electrical Engineering , Computer Science , Decision Systems , Talented Youth , Case Western , Marshall Scholarship , Professor Michael , Bayesian Inference , Machine Learning , It Womens Technology Program ,

Automated method helps researchers quantify uncertainty in their predictions

A new technique can help researchers who use Bayesian inference achieve more accurate results more quickly, without a lot of additional work. ....

Tamara Broderick , Martin Ingram , Andrew Gelman , Bryan Giordano , Us Office Of Naval Research , Department Of Electrical Engineering , University Of California At Berkeley , National Science Foundation , Laboratory For Information , Columbia University , Institute For Data , Journal Of Machine Learning Research , Electrical Engineering , Computer Science , Decision Systems , Machine Learning , Bayesian Inference , Sample Average Approximation , Eterministic Advi ,