### Distributed Solar: The Democratizaton of Energy

### Blogroll

- Why It’s So Freaking Hard To Make A Good COVID-19 Model Five Thirty Eight’s take on why pandemic modeling is so difficult
- GeoEnergy Math Prof Paul Pukite’s Web site devoted to energy derived from geological and geophysical processes and categorized according to its originating source.
- Mark Berliner's video lecture "Bayesian mechanistic-statistical modeling with examples in geophysical settings"
- Harvard's Project Implicit
- Hermann Scheer Hermann Scheer was a visionary, a major guy, who thought deep thoughts about energy, and its implications for humanity’s relationship with physical reality
- Lenny Smith's CHAOS: A VERY SHORT INTRODUCTION This is a PDF version of Lenny Smith’s book of the same title, also available from Amazon.com
- Tim Harford's “More or Less'' Tim Harford explains – and sometimes debunks – the numbers and statistics used in political debate, the news and everyday life
- Simon Wood's must-read paper on dynamic modeling of complex systems I highlighted Professor Wood’s paper in https://hypergeometric.wordpress.com/2014/12/26/struggling-with-problems-already-attacked/
- Leverhulme Centre for Climate Change Mitigation
- Mertonian norms
- Musings on Quantitative Paleoecology Quantitative methods and palaeoenvironments.
- Dominic Cummings blog Chief advisor to the PM, United Kingdom
- Ives and Dakos techniques for regime changes in series
- American Statistical Association
- ggplot2 and ggfortify Plotting State Space Time Series with ggplot2 and ggfortify
- All about ENSO, and lunar tides (Paul Pukite) Historically, ENSO has been explained in terms of winds. But recently — and Dr Paul Pukite has insisted upon this for a long time — the oscillation of ENSO has been explained as a large-scale slosh due to lunar tidal forcing.
- All about models
- Leadership lessons from Lao Tzu
- Mrooijer's Numbers R 4Us
- Flettner Rotor Bruce Yeany introduces the Flettner Rotor and related science
- Brendon Brewer on Overfitting Important and insightful presentation by Brendon Brewer on overfitting
- Higgs from AIR describing NAO and EA Stephanie Higgs from AIR Worldwide gives a nice description of NAO and EA in the context of discussing “The Geographic Impact of Climate Signals on European Winter Storms”
- Subsidies for wind and solar versus subsidies for fossil fuels
- The Keeling Curve: its history History of the Keeling Curve and Charles David Keeling
- OOI Data Nuggets OOI Ocean Data Lab: The Data Nuggets
- Label Noise
- What If
- Darren Wilkinson's introduction to ABC Darren Wilkinson’s introduction to approximate Bayesian computation (“ABC”). See also his post about summary statistics for ABC https://darrenjw.wordpress.com/2013/09/01/summary-stats-for-abc/
- Beautiful Weeds of New York City
- Slice Sampling
- Logistic curves in market disruption From DollarsPerBBL, about logistic or S-curves as models of product take-up rather than exponentials, with notes on EVs
- James' Empty Blog
- Tony Seba Solar energy, electric vehicle, energy storage, and business disruption professor and visionary
- Gabriel's staircase
- Why "naive Bayes" is not Bayesian Explains why the so-called “naive Bayes” classifier is not Bayesian. The setup is okay, but estimating probabilities by doing relative frequencies instead of using Dirichlet conjugate priors or integration strays from The Path.
- Thaddeus Stevens quotes As I get older, I admire this guy more and more
- Busting Myths About Heat Pumps Heat pumps are perhaps the most efficient heating and cooling systems available. Recent literature distributed by utilities hawking natural gas and other sources use performance figures from heat pumps as they were available 15 years ago. See today’s.
- Peter Congdon's Bayesian statistical modeling Peter Congdon’s collection of links pertaining to his several books on Bayesian modeling
- Comprehensive Guide to Bayes Rule
- John Kruschke's "Dong Bayesian data analysis" blog Expanding and enhancing John’s book of same title (now in second edition!)
- Pat's blog While it is described as “The mathematical (and other) thoughts of a (now retired) math teacher”, this is false humility, as it chronicles the present and past life and times of mathematicians in their context. Recommended.
- Team Andrew Weinberg Walking September 8th for the Jimmy Fund!
- distributed solar and matching location to need
- American Association for the Advancement of Science (AAAS)
- AP Statistics: Sampling, by Michael Porinchak Twin City Schools
- Earle Wilson
- WEAPONS OF MATH DESTRUCTION Cathy O’Neil’s WEAPONS OF MATH DESTRUCTION,
- "Consider a Flat Pond" Invited talk introducing systems thinking, by Jan Galkowski, at First Parish in Needham, UU, via Zoom
- "Perpetual Ocean" from NASA GSFC
- Charlie Kufs' "Stats With Cats" blog “You took Statistics 101. Now what?”

### climate change

- The Scientific Case for Modern Human-caused Global Warming
- RealClimate
- The Sunlight Economy
- Équiterre Equiterre helps build a social movement by encouraging individuals, organizations and governments to make ecological and equitable choices, in a spirit of solidarity.
- All Models Are Wrong Dr Tamsin Edwards blog about uncertainty in science, and climate science
- "Warming Slowdown?" (part 2 of 2) The idea of a global warming slowdown or hiatus is critically examined, emphasizing the literature, the datasets, and means and methods for telling such. The second part.
- Wally Broecker on climate realism
- CLIMATE ADAM Previously from the Science news staff at the podcast of Nature (“Nature Podcast”), the journal, now on YouTube, encouraging climate action through climate comedy.
- US$165/tonne CO2: Sweden Sweden has a Carbon Dioxide tax of US$165 per tonne at present. CO2 tax was imposed in 1991. GDP has grown 60%.
- Energy payback period for solar panels Considering everything, how long do solar panels have to operate to offset the energy used to produce them?
- Documenting the Climate Deniarati at work
- On Thomas Edison and Solar Electric Power
- Model state level energy policy for New Englad Bob Massie’s proposed energy policy for Massachusetts, an admirable model for energy policy anywhere in New England
- "Mighty Microgrids" Webinar This is a Webinar on YouTube about Microgrids from the Institute for Local Self-Reliance (ILSR), featuring New York State and Minnesota
- SolarLove
- Transitioning to fully renewable energy Professor Saul Griffiths talks to transitioning the customer journey, from a dependency upon fossil fuels to an electrified future
- The HUMAN-caused greenhouse effect, in under 5 minutes, by Bill Nye
- Mrooijer's Global Temperature Explorer
- Berkeley Earth Surface Temperature
- David Appell's early climate science
- Climate Change: A health emergency … New England Journal of Medicine Caren G. Solomon, M.D., M.P.H., and Regina C. LaRocque, M.D., M.P.H., January 17, 2019 N Engl J Med 2019; 380:209-211 DOI: 10.1056/NEJMp1817067
- And Then There's Physics
- Tell Utilities Solar Won't Be Killed Barry Goldwater, Jr’s campaign to push for solar expansion against monopolistic utilities, as a Republican
- The Green Plate Effect Eli Rabett’s “The Green Plate Effect”
- Rabett Run Incisive analysis of climate science versus deliberate distraction
- Climate Communication Hassol, Somerville, Melillo, and Hussin site communicating climate to the public
- “The discovery of global warming'' (American Institute of Physics)
- An open letter to Steve Levitt
- Paul Beckwith Professor Beckwith is, in my book, one of the most insightful and analytical observers on climate I know. I highly recommend his blog, and his other informational products.
- MIT's Climate Primer
- "Lessons of the Little Ice Age" (Farber) From Dan Farber, at LEGAL PLANET
- `Who to believe on climate change': Simple checks By Bart Verheggen
- Ray Pierrehumbert's site related to "Principles of Planetary Climate" THE book on climate science
- Social Cost of Carbon
- Ice and Snow
- Climate Change Denying Organizations
- Simple box models and climate forcing IMO one of Tamino’s best posts illustrating climate forcing using simple box models
- “Ways to [try to] slow the Solar Century''
- Simple models of climate change
- Jacobson WWS literature index
- Warming slowdown discussion
- "A field guide to the climate clowns"
- Andy Zucker's "Climate Change and Psychology"
- "Getting to the Energy Future We Want," Dr Steven Chu
- Reanalyses.org
- World Weather Attribution
- Mathematics and Climate Research Network The Mathematics and Climate Research Network (MCRN) engages mathematicians to collaborating on the cryosphere, conceptual model validation, data assimilation, the electric grid, food systems, nonsmooth systems, paleoclimate, resilience, tipping points.
- The beach boondoggle Prof Rob Young on how owners of beach property are socializing their risks at costs to all of us, not the least being it seems coastal damage is less than it actually is
- HotWhopper: It's excellent. Global warming and climate change. Eavesdropping on the deniosphere, its weird pseudo-science and crazy conspiracy whoppers.
- Dessler's 6 minute Greenhouse Effect video

### Archives

### Jan Galkowski

# Category Archives: probabilistic programming

## Cory Lesmeister’s treatment of Simson’s Paradox (at “Fear and Loathing in Data Science”)

(Updated 2016-05-08, to provide reference for plateaus of ML functions in vicinity of MLE.) Simpson’s Paradox is one of those phenomena of data which really give Statistics a substance and a role, beyond the roles it inherits from, say, theoretical … Continue reading

Posted in Akaike Information Criterion, approximate Bayesian computation, Bayes, Bayesian, evidence, Frequentist, games of chance, information theoretic statistics, Kalman filter, likelihood-free, mathematics, maths, maximum likelihood, Monte Carlo Statistical Methods, probabilistic programming, rationality, Rauch-Tung-Striebel, Simpson's Paradox, state-space models, statistical dependence, statistics, stochastics
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## “Grid shading by simulated annealing” [Martyn Plummer]

Source: Grid shading by simulated annealing (or what I did on my holidays), aka “fun with GCHQ job adverts”, by Martyn Plummer, developer of JAGS. Excerpt: I wanted to solve the puzzle but did not want to sit down with … Continue reading

Posted in approximate Bayesian computation, Bayesian, Bayesian inversion, Boltzmann, BUGS, Christian Robert, Gibbs Sampling, JAGS, likelihood-free, Markov Chain Monte Carlo, Martyn Plummer, mathematics, maths, MCMC, Monte Carlo Statistical Methods, optimization, probabilistic programming, SPSA, stochastic algorithms, stochastic search
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## Generating supports for classification rules in black box regression models

Inspired by the extensive and excellent work in approximate Bayesian computation (see also), especially that done by Professors Christian Robert and colleagues (see also), and Professor Simon Wood (see also), it occurred to me that the complaints regarding lack of … Continue reading

Posted in approximate Bayesian computation, Bayes, Bayesian, Bayesian inversion, generalized linear models, machine learning, numerical analysis, numerical software, probabilistic programming, rationality, reasonableness, state-space models, statistics, stochastic algorithms, stochastic search, stochastics, support of black boxes
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## What the future of energy everywhere looks like

What will the energy landscape look like after utility companies are either dead, dying, or revert to a tiny portion of their territory? Silicon Valley CCE Partnership gives us all a clue. It’s been described in the San Francisco Chronicle, … Continue reading

Posted in adaptation, Anthropocene, capricious gods, chance, citizenship, civilization, clean disruption, conservation, consumption, decentralized electric power generation, decentralized energy, demand-side solutions, destructive economic development, dynamical systems, economics, efficiency, energy, energy reduction, energy utilities, engineering, environment, ethics, forecasting, fossil fuel divestment, geophysics, global warming, Hyper Anthropocene, investment in wind and solar energy, living shorelines, mesh models, meteorology, microgrids, mitigation, obfuscating data, oceanography, physical materialism, physics, pipelines, planning, politics, prediction, probabilistic programming, public utility commissions, PUCs, quantum, reasonableness, reproducible research, risk, Sankey diagram, science, sea level rise, selfishness, solar energy, solar power, SolarPV.tv, Spaceship Earth, statistics, stochastic algorithms, stochastics, Svante Arrhenius, taxes, temporal myopia, the right to know, the value of financial assets, transparency, UU Humanists, WHOI, wind energy, wind power, zero carbon
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## Solar array with cloud predicting technology launched in WA

Australia’s first grid-connected solar power project with cloud predicting technology launched at Karratha Airport, WA, in bid to smooth solar supply. Source: Solar array with cloud predicting technology launched in WA

Posted in adaptation, Anthropocene, carbon dioxide, citizenship, civilization, clean disruption, climate, climate change, climate disruption, conservation, consumption, decentralized electric power generation, decentralized energy, demand-side solutions, dynamic linear models, efficiency, energy, energy reduction, energy utilities, engineering, environment, ethics, forecasting, geophysics, global warming, Hyper Anthropocene, investment in wind and solar energy, Kalman filter, mathematics, maths, meteorology, microgrids, mitigation, NCAR, numerical software, optimization, physics, prediction, probabilistic programming, rationality, reasonableness, risk, science, solar power, stochastics, sustainability, time series
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## Thank You

Originally posted on Open Mind:

To all the readers who make this blog worth writing: Thank you. Thank you for sharing my work. One of the things that makes me proud is that often my blog posts are used as…

Posted in astrophysics, citizen science, climate change, climate data, climate disruption, climate education, climate models, differential equations, dynamical systems, ecology, ensembles, forecasting, games of chance, geophysics, global warming, hiatus, Hyper Anthropocene, IPCC, mathematics, mathematics education, maths, meteorology, model comparison, new forms of scientific peer review, open data, open source scientific software, physics, probabilistic programming, probability, rationality, reasonableness, reproducible research, risk, science, science education, spatial statistics, statistics, Tamino, the right to know, time series, transparency
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## “Cauchy Distribution: Evil or Angel?” (from Xian)

Cauchy Distribution: Evil or Angel?. From Professor Christian Robert.

## “A vignette on Metropolis” (Christian Robert)

Originally posted on Xi'an's Og:

Over the past week, I wrote a short introduction to the Metropolis-Hastings algorithm, mostly in the style of our Introduction to Monte Carlo with R book, that is, with very little theory and…

## “Unbiased Bayes for Big Data: Path of partial posteriors” (Christian Robert)

Unbiased Bayes for Big Data: Path of partial posteriors.

## Christian Robert on the amazing Gibbs sampler

Professor Christian Robert remarks on the amazing Gibbs sampler. Implicitly he’s also underscoring the power of properly done Bayesian computational analysis. For here we have a problem with a posterior distribution having two strong modes, so a point estimate, like … Continue reading

## The designers of our climate

Originally posted on …and Then There's Physics:

Okay, I finally succumbed and actually waded through some of the new paper by Monckton, Soon, Legates & Briggs called Why models run hot: results from an irreducibly simple climate model. I…

Posted in astrophysics, bridge to nowhere, carbon dioxide, carbon dioxide capture, carbon dioxide sequestration, Carbon Tax, Carl Sagan, citizenship, civilization, climate, climate change, climate education, differential equations, ecology, economics, engineering, environment, ethics, forecasting, fossil fuel divestment, geoengineering, geophysics, humanism, IPCC, mathematics, mathematics education, maths, meteorology, methane, NASA, NCAR, Neill deGrasse Tyson, NOAA, oceanography, open data, open source scientific software, physics, politics, population biology, Principles of Planetary Climate, probabilistic programming, R, rationality, reasonableness, reproducible research, risk, science, science education, scientific publishing, sociology, solar power, statistics, testing, the right to know
1 Comment

## Heads-up limit hold’em poker is solved

This is from today’s news in Science. The full citation is: M. Bowling, N. Burch, M. Johanson, O. Tammelin, “Heads-up limit hold’em poker is solved”, Science, 9 January 2015, 347(6218), 145-149, http://dx.doi.org/10.1126/science.1259433. See also a University of Alberta site where … Continue reading

Posted in games of chance, probabilistic programming, risk
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## On nested equivalence classes of climate models, ordered by computational complexity

I’m digging into the internals of ABC, for professional and scientific reasons. I’ve linked a great tutorial elsewhere, and argued that this framework, advanced by Wood, and Wilkinson (Robert), and Wilkinson (Darren), and Hartig and colleagues, and Robert and colleagues, … Continue reading

Posted in approximate Bayesian computation, Bayes, Bayesian, biology, ecology, environment, forecasting, geophysics, IPCC, mathematics, maths, MCMC, meteorology, NCAR, NOAA, oceanography, optimization, population biology, Principles of Planetary Climate, probabilistic programming, R, science, stochastic algorithms, stochastic search
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## “[W]e want to model the process as we would simulate it.”

Professor Darren Wilkinson offers a pithy insight on how to go about constructing statistical models, notably hierarchical ones: “… we want to model the process as we would simulate it ….” This appears in his blog post One-way ANOVA with … Continue reading

Posted in approximate Bayesian computation, Bayes, Bayesian, biology, ecology, engineering, forecasting, mathematics, mathematics education, maths, model comparison, optimization, population biology, probabilistic programming, rationality, reasonableness, risk, science, science education, sociology, statistics, stochastic algorithms
Tagged ANOVA
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## struggling with problems already partly solved by others

Climate modelers and models see as their frontier the problem of dealing with spontaneous dynamics in systems such as atmosphere or ocean which are not directly forced by boundary conditions such as radiative forcing due to increased greenhouse gas (“GHG”) … Continue reading

Posted in approximate Bayesian computation, Bayes, Bayesian, biology, climate, climate education, differential equations, ecology, engineering, environment, geophysics, IPCC, mathematics, mathematics education, meteorology, model comparison, NCAR, NOAA, oceanography, physics, population biology, probabilistic programming, rationality, reasonableness, risk, science, science education, statistics, stochastic algorithms, stochastic search
1 Comment

## illustrating particle filters and Bayesian fusion using successive location estimates on the unit circle

Introduction Modern treatments of Bayesian integration to obtain posterior densities often use some form of Markov Chain Monte Carlo (“MCMC”), typically Gibbs sampling. Gibbs works well with many Bayesian hierarchical models. The standard problem-solving situation with these is that a … Continue reading

## An equation-free introduction to Bayesian inference

By Tomoharu Eguchi from 2008: “An Introduction to Bayesian Statistics Without Using Equations“.

## example of Bayesian inversion

This is based upon my solution of Exercise 2.3, page 18, R. Christensen, W. Johnson, A. Branscum, T. E. Hanson, Bayesian Ideas and Data Analysis, Chapman & Hall, 2011. The purpose is to show how information latent in a set … Continue reading

## Bayesian deconvolution of stick lengths

Consider trying to determine the length of a straight stick. Instead of the measurement errors being clustered about zero, suppose the errors are known to be always positive, that is, no measurement ever underestimates the length of the stick. Such … Continue reading

## Blind Bayesian recovery of components of residential solid waste tonnage from totals data

This is a sketch of how maths and statistics can do something called blind source separation, meaning to estimate the components of data given only their totals. Here, I use Bayesian techniques for the purpose, sometimes called Bayesian inversion, using … Continue reading

## “The joy and martyrdom of trying to be a Bayesian”

Bayesians have all been there. Some of us don’t depend upon producing publications to assure our pay, so we less have the pressure of pleasing peer reviewers. Nonetheless, it’s all reacting to “What the hell are you doing? I don’t … Continue reading

## How fast is JAGS?

How fast is JAGS?.

## Comment on “How urban anonymity disappears when all data is tracked”, an article in the NY Times

The New York Times has an article titled “How urban anonymity disappears when all data is tracked” by Quentin Hardy which appears in its “Bits” section. I just posted a comment on that article, which is reproduced below: I hope … Continue reading

## “The most common fallacy in discussing extreme weather events”: Stefan Rahmstorf

The most common fallacy in discussing extreme weather events.

## Dr David Gallo of WHOI on today’s “Face the Nation” on CBS: MH370

Good to see Dr Dave Gallo speaking about WHOI’s approach to AF447 and its similarity to MH370. Update. 2014-03-26. WHOI is getting ready to deploy their REMUS 6000 systems. Update. 2014-03-28. The Woods Hole Oceanographic Institution has offered its expertise … Continue reading

Posted in engineering, history, meteorology, oceanography, probabilistic programming, WHOI
Tagged AF447, MH370
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## The zero-crossings trick for JAGS: Finding roots stochastically

BUGS has a “zeros trick” (Lund, Jackson, Best, Thomas, Spiegelhalter, 2013, pages 204-206; see also an online illustration) for specifying a new distribution which is not in the standard set. The idea is to couple an invented-for-the-moment Poisson density to … Continue reading

Posted in Bayesian, BUGS, education, forecasting, Gibbs Sampling, JAGS, mathematics, MCMC, probabilistic programming, R, statistics, stochastic search
Tagged error-in-variables problem, optimization, zeros trick
4 Comments

## “Data-driven science is a failure of imagination” (Petr Keil)

Happened across this today … I could not agree more: “Data-driven science is a failure of imagination” by Petr Keil. I look forward to reading his posts on Bayesian statistics.

Posted in Bayesian, engineering, history, mathematics, maths, MCMC, probabilistic programming, rationality, science
Tagged data mining, data science
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