### Distributed Solar: The Democratizaton of Energy

### Blogroll

- Leverhulme Centre for Climate Change Mitigation
- Patagonia founder Yvon Chouinard on how businesses can help our collective environmental mess
- Number Cruncher Politics
- GeoEnergy Math
- SASB
- Higgs from AIR describing NAO and EA
- Brendon Brewer on Overfitting
- Mike Bloomberg, 2020
- Prediction vs Forecasting: Knaub
- Why It’s So Freaking Hard To Make A Good COVID-19 Model

### climate change

- "Mighty Microgrids" Webinar
- Jacobson WWS literature index
- "Impacts of Green New Deal energy plans on grid stability, costs, jobs, health, and climate in 143 countries" (Jacobson, Delucchi, Cameron, et al)
- Sea Change Boston
- Documenting the Climate Deniarati at work
- Spectra Energy exposed
- Climate impacts on retail and supply chains
- Warming slowdown discussion
- `Who to believe on climate change': Simple checks
- Mrooijer's Global Temperature Explorer

### Archives

### Jan Galkowski

# Category Archives: linear regression

## 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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## Reanalysis of business visits from deployments of a mobile phone app

Updated, 20th October 2020 This reports a reanalysis of data from the deployment of a mobile phone app, as reported in: M. Yauck, L.-P. Rivest, G. Rothman, “Capture-recapture methods for data on the activation of applications on mobile phones“, Journal … Continue reading

Posted in Bayesian computational methods, biology, capture-mark-recapture, capture-recapture, Christian Robert, count data regression, cumulants, diffusion, diffusion processes, Ecological Society of America, ecology, epidemiology, experimental science, field research, Gibbs Sampling, Internet measurement, Jean-Michel Marin, linear regression, mark-recapture, mathematics, maximum likelihood, Monte Carlo Statistical Methods, multilist methods, multivariate statistics, non-mechanistic modeling, non-parametric statistics, numerics, open source scientific software, Pierre-Simon Laplace, population biology, population dynamics, quantitative biology, quantitative ecology, R, R statistical programming language, sampling, sampling algorithms, segmented package in R, statistical ecology, statistical models, statistical regression, statistical series, statistics, stepwise approximation, stochastic algorithms, surveys, V. M. R. Muggeo
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