Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...
Synthetic dataset outputs for public analysis without privacy risk. Part of my current workflow as survey leader of the Data Engineering Pilipinas group. Comparable distributions per column: based on ...
They look, move and even smell like the kind of furry Everglades marsh rabbit a Burmese python would love to eat. But these bunnies are robots meant to lure the giant invasive snakes out of their ...
ABSTRACT: Regression models with intractable normalizing constants are valuable tools for analyzing complex data structures, yet parameter inference for such models remains highly ...
Abstract: In this study, comprehensive approximate Bayesian computation (ABC) technique is explored, and develop for an innovative model. We practically demonstrate approximate Bayesian computation ...
Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...
ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...
How relevant is the prior? Bayesian causal inference for dynamic perception in volatile environments
Not revised: This Reviewed Preprint includes the authors’ original preprint (without revision), an eLife assessment, and public reviews. Behavioural adjustments to different sources of uncertainty ...
ABSTRACT: Geographical variations in all-cause mortality rates may be influenced by residents’ place of residence and the time period under study. Understanding these variations is essential for ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results