### Distributed Solar: The Democratizaton of Energy

### Blogroll

- John Kruschke's "Dong Bayesian data analysis" blog
- Brian McGill's Dynamic Ecology blog
- "The Expert"
- All about Sankey diagrams
- International Society for Bayesian Analysis (ISBA)
- Bob Altemeyer on authoritarianism (via Dan Satterfield)
- Fear and Loathing in Data Science
- Comprehensive Guide to Bayes Rule
- Mark Berliner's video lecture "Bayesian mechanistic-statistical modeling with examples in geophysical settings"
- Why "naive Bayes" is not Bayesian

### climate change

- RealClimate
- Andy Zucker's "Climate Change and Psychology"
- "Warming Slowdown?" (part 1 of 2)
- "Climate science is setttled enough"
- Sea Change Boston
- Berkeley Earth Surface Temperature
- Exxon-Mobil statement on UNFCCC COP21
- Grid parity map for Solar PV in United States
- Professor Robert Strom's compendium of resources on climate change
- US$165/tonne CO2: Sweden

### Archives

### Jan Galkowski

# Category Archives: numerical linear algebra

## Phase Plane plots of COVID-19 deaths *with uncertainties*

I. Introduction. It’s time to fulfill the promise made in “Phase plane plots of COVID-19 deaths“, a blog post from 2nd May 2020, and produce the same with uncertainty clouds about the functional trajectories(*). To begin, here are some assumptions … Continue reading

Posted in American Statistical Association, Andrew Harvey, anomaly detection, count data regression, COVID-19, dependent data, dlm package, Durbin and Koopman, dynamic linear models, epidemiology, filtering, forecasting, Kalman filter, LaTeX, model-free forecasting, Monte Carlo Statistical Methods, numerical algorithms, numerical linear algebra, population biology, population dynamics, prediction, R, R statistical programming language, regression, statistical learning, stochastic algorithms
Tagged prediction intervals
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## The Johnson-Lindenstrauss Lemma, and the paradoxical power of random linear operators. Part 1.

Updated, 2018-12-04 I’ll be discussing the ramifications of: William B. Johnson and Joram Lindenstrauss, “Extensions of Lipschitz mappings into a Hilbert space, Contemporary Mathematics, 26:189–206, 1984. for several posts here. Some introduction and links to proofs and explications will be … Continue reading

Posted in clustering, data science, dimension reduction, information theoretic statistics, Johnson-Lindenstrauss Lemma, k-NN, Locality Sensitive Hashing, mathematics, maths, multivariate statistics, non-parametric model, numerical algorithms, numerical linear algebra, point pattern analysis, random projections, recommender systems, science, stochastic algorithms, stochastics, subspace projection methods
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## 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