Distributed Solar: The Democratizaton of Energy
Blogroll
- Team Andrew Weinberg Walking September 8th for the Jimmy Fund!
- Mark Berliner's video lecture "Bayesian mechanistic-statistical modeling with examples in geophysical settings"
- The Plastic Pick-Up: Discovering new sources of marine plastic pollution
- Flettner Rotor Bruce Yeany introduces the Flettner Rotor and related science
- AP Statistics: Sampling, by Michael Porinchak Twin City Schools
- Earth Family Alpha Michael Osborne’s blog (former Executive at Austin Energy, now Chairman of the Electric Utility Commission for Austin, Texas)
- 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.
- All about models
- ggplot2 and ggfortify Plotting State Space Time Series with ggplot2 and ggfortify
- NCAR AtmosNews
- Charlie Kufs' "Stats With Cats" blog “You took Statistics 101. Now what?”
- Tony Seba Solar energy, electric vehicle, energy storage, and business disruption professor and visionary
- Mike Bloomberg, 2020 He can get progress on climate done, has the means and experts to counter the Trump and Republican digital disinformation machine, and has the experience, knowledge, and depth of experience to achieve and unify.
- Nadler Strategy, LLC, on sustainability Thinking about business, efficient and effective management, and business value
- What If
- Leadership lessons from Lao Tzu
- Label Noise
- Dominic Cummings blog Chief advisor to the PM, United Kingdom
- 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
- Giant vertical monopolies for energy have stopped making sense
- Ives and Dakos techniques for regime changes in series
- American Association for the Advancement of Science (AAAS)
- Dollars per BBL: Energy in Transition
- 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
- The Mermaid's Tale A conversation about biological complexity and evolution, and the societal aspects of science
- WEAPONS OF MATH DESTRUCTION, reviews Reviews of Cathy O’Neil’s new book
- 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”
- 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.
- John Kruschke's "Dong Bayesian data analysis" blog Expanding and enhancing John’s book of same title (now in second edition!)
- Brian McGill's Dynamic Ecology blog Quantitative biology with pithy insights regarding applications of statistical methods
- Fear and Loathing in Data Science Cory Lesmeister’s savage journey to the heart of Big Data
- The Alliance for Securing Democracy dashboard
- Dr James Spall's SPSA
- Bob Altemeyer on authoritarianism (via Dan Satterfield) The science behind the GOP civil war
- Beautiful Weeds of New York City
- Rasmus Bååth's Research Blog Bayesian statistics and data analysis
- Earth Family Beta MIchael Osborne’s blog on Science and the like
- Mertonian norms
- Survey Methodology, Prof Ron Fricker http://faculty.nps.edu/rdfricke/
- 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.
- The Keeling Curve: its history History of the Keeling Curve and Charles David Keeling
- BioPython A collection of Python tools for quantitative Biology
- Risk and Well-Being
- Gavin Simpson
- Los Alamos Center for Bayesian Methods
- Ted Dunning
- Subsidies for wind and solar versus subsidies for fossil fuels
- Number Cruncher Politics
- Prediction vs Forecasting: Knaub “Unfortunately, ‘prediction,’ such as used in model-based survey estimation, is a term that is often subsumed under the term ‘forecasting,’ but here we show why it is important not to confuse these two terms.”
climate change
- The net average effect of a warming climate is increased aridity (Professor Steven Sherwood)
- James Powell on sampling the climate consensus
- Ricky Rood's “What would happen to climate if we (suddenly) stopped emitting GHGs today?
- The Green Plate Effect Eli Rabett’s “The Green Plate Effect”
- "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
- Climate change: Evidence and causes A project of the UK Royal Society: (1) Answers to key questions, (2) evidence and causes, and (3) a short guide to climate science
- "Betting strategies on fluctuations in the transient response of greenhouse warming" By Risbey, Lewandowsky, Hunter, Monselesan: Betting against climate change on durations of 15+ years is no longer a rational proposition.
- The Scientific Case for Modern Human-caused Global Warming
- Isaac Held's blog In the spirit of Ray Pierrehumbert’s “big ideas come from small models” in his textbook, PRINCIPLES OF PLANETARY CLIMATE, Dr Held presents quantitative essays regarding one feature or another of the Earth’s climate and weather system.
- "Climate science is setttled enough"
- Thriving on Low Carbon
- James Hansen and granddaughter Sophie on moving forward with progress on climate
- Ray Pierrehumbert's site related to "Principles of Planetary Climate" THE book on climate science
- "Lessons of the Little Ice Age" (Farber) From Dan Farber, at LEGAL PLANET
- The HUMAN-caused greenhouse effect, in under 5 minutes, by Bill Nye
- "When Did Global Warming Stop" Doc Snow’s treatment of the denier claim that there’s been no warming for the most recent N years. (See http://hubpages.com/@doc-snow for more on him.)
- Solar Gardens Community Power
- Climate Communication Hassol, Somerville, Melillo, and Hussin site communicating climate to the public
- 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
- Dessler's 6 minute Greenhouse Effect video
- Nick Bower's "Scared Scientists"
- HotWhopper: It's excellent. Global warming and climate change. Eavesdropping on the deniosphere, its weird pseudo-science and crazy conspiracy whoppers.
- World Weather Attribution
- Professor Robert Strom's compendium of resources on climate change Truly excellent
- Climate Change Reports By John and Mel Harte
- Berkeley Earth Surface Temperature
- Klaus Lackner (ASU), Silicon Kingdom Holdings (SKH) Capturing CO2 from air at scale
- Updating the Climate Science: What path is the real world following? From Professors Makiko Sato & James Hansen of Columbia University
- `Who to believe on climate change': Simple checks By Bart Verheggen
- “Ways to [try to] slow the Solar Century''
- Tell Utilities Solar Won't Be Killed Barry Goldwater, Jr’s campaign to push for solar expansion against monopolistic utilities, as a Republican
- Skeptical Science
- Simple box models and climate forcing IMO one of Tamino’s best posts illustrating climate forcing using simple box models
- Social Cost of Carbon
- History of discovering Global Warming From the American Institute of Physics.
- Risk and Well-Being
- Climate model projections versus observations
- 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 Keeling Curve The first, and one of the best programs for creating a spatially significant long term time series of atmospheric concentrations of CO2. Started amongst great obstacles by one, smart determined guy, Charles David Keeling.
- Rabett Run Incisive analysis of climate science versus deliberate distraction
- The Carbon Cycle The Carbon Cycle, monitored by The Carbon Project
- Climate Change Denying Organizations
- Warming slowdown discussion
- Anti—Anti-#ClimateEmergency Whether to declare a climate emergency is debatable. But some critics have gone way overboard.
- 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.
- Mrooijer's Global Temperature Explorer
- Tamino's Open Mind Open Mind: A statistical look at climate, its science, and at science denial
- Andy Zucker's "Climate Change and Psychology"
- All Models Are Wrong Dr Tamsin Edwards blog about uncertainty in science, and climate science
- Eli on the spectroscopic basis of atmospheric radiation physical chemistry
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
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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
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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
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“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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ecoquant
667 per centimeter : climate science, quantitative biology, statistics, and energy policy 