Distributed Solar: The Democratizaton of Energy
Blogroll
- Why "naive Bayes" is not Bayesian
- John Cook's reasons to use Bayesian inference
- Label Noise
- Earle Wilson
- All about ENSO, and lunar tides (Paul Pukite)
- WEAPONS OF MATH DESTRUCTION, reviews
- Busting Myths About Heat Pumps
- Mike Bloomberg, 2020
- distributed solar and matching location to need
- Rasmus Bååth's Research Blog
climate change
- Climate Change Reports
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- The Green Plate Effect
- Eli on the spectroscopic basis of atmospheric radiation physical chemistry
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- US$165/tonne CO2: Sweden
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Archives
Jan Galkowski
Category Archives: Calculus
Calculating Derivatives from Random Forests
(Comment on prediction intervals for random forests, and links to a paper.) (Edits to repair smudges, 2020-06-28, about 0945 EDT. Closing comment, 2020-06-30, 1450 EDT.) There are lots of ways of learning about mathematical constructs, even about actual machines. One … Continue reading
Posted in bridge to somewhere, Calculus, dependent data, dynamic generalized linear models, dynamical systems, ensemble methods, ensemble models, filtering, forecasting, hierarchical clustering, linear regression, model-free forecasting, Monte Carlo Statistical Methods, non-mechanistic modeling, non-parametric model, non-parametric statistics, numerical algorithms, prediction, R statistical programming language, random forests, regression, sampling, splines, statistical learning, statistical series, statistics, time derivatives, time series
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Cumulants and the Cornish-Fisher Expansion
“Consider the following.” (Bill Nye the Science Guy) There are random variables drawn from the same kind of probability distribution, but with different parameters for each. In this example, I’ll consider random variables , that is, each drawn from a … Continue reading
When linear systems can’t be solved by linear means
Linear systems of equations and their solution form the cornerstone of much Engineering and Science. Linear algebra is a paragon of Mathematics in the sense that its theory is what mathematicians try to emulate when they develop theory for many … Continue reading
Merry Newtonmas tomorrow! On finding the area of the Batman Shape using Monte Carlo integration
It’s Newtonmas 2017 tomorrow! What better way to celebrate than talk about integration! The Batman Shape (sometimes called the Batman Curve, somewhat erroneously, I think) looks like this: You can find details about it at Wolfram MathWorld, including its area … Continue reading
Posted in Bayes, Calculus, Markov Chain Monte Carlo
Tagged Batman Curve, Batman Shape, James Schloss, Monte Carlo integration, slice sampling
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