Distributed Solar: The Democratizaton of Energy
Blogroll
- Fear and Loathing in Data Science Cory Lesmeister’s savage journey to the heart of Big Data
- Dominic Cummings blog Chief advisor to the PM, United Kingdom
- All about models
- Tony Seba Solar energy, electric vehicle, energy storage, and business disruption professor and visionary
- Carl Safina's blog One of the wisest on Earth
- Professor David Draper
- What If
- South Shore Recycling Cooperative Materials management, technical assistance and networking, town advocacy, public outreach
- "Perpetual Ocean" from NASA GSFC
- 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
- ggplot2 and ggfortify Plotting State Space Time Series with ggplot2 and ggfortify
- OOI Data Nuggets OOI Ocean Data Lab: The Data Nuggets
- Team Andrew Weinberg Walking September 8th for the Jimmy Fund!
- "The Expert"
- AP Statistics: Sampling, by Michael Porinchak Twin City Schools
- Comprehensive Guide to Bayes Rule
- Mertonian norms
- 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/
- American Association for the Advancement of Science (AAAS)
- "Talking Politics" podcast David Runciman, Helen Thompson
- Leverhulme Centre for Climate Change Mitigation
- SASB Sustainability Accounting Standards Board
- In Monte Carlo We Trust The statistics blog of Matt Asher, actually called the “Probability and Statistics Blog”, but his subtitle is much more appealing. Asher has a Manifesto at http://www.statisticsblog.com/manifesto/.
- James' Empty Blog
- Gabriel's staircase
- International Society for Bayesian Analysis (ISBA)
- BioPython A collection of Python tools for quantitative Biology
- "Impacts of Green New Deal energy plans on grid stability, costs, jobs, health, and climate in 143 countries" (Jacobson, Delucchi, Cameron, et al) Global warming, air pollution, and energy insecurity are three of the greatest problems facing humanity. To address these problems, we develop Green New Deal energy roadmaps for 143 countries.
- 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.
- Subsidies for wind and solar versus subsidies for fossil fuels
- NCAR AtmosNews
- Dollars per BBL: Energy in Transition
- American Statistical Association
- Harvard's Project Implicit
- Flettner Rotor Bruce Yeany introduces the Flettner Rotor and related science
- Ives and Dakos techniques for regime changes in series
- Beautiful Weeds of New York City
- 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
- The Keeling Curve: its history History of the Keeling Curve and Charles David Keeling
- 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.
- Label Noise
- 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
- Quotes by Nikola Tesla Quotes by Nikola Tesla, including some of others he greatly liked.
- Brian McGill's Dynamic Ecology blog Quantitative biology with pithy insights regarding applications of statistical methods
- 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.”
- 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
- 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 Plastic Pick-Up: Discovering new sources of marine plastic pollution
- Giant vertical monopolies for energy have stopped making sense
- 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
climate change
- Tuft's Professor Kenneth Lang on the physical chemistry of the Greenhouse Effect
- Berkeley Earth Surface Temperature
- "A field guide to the climate clowns"
- All Models Are Wrong Dr Tamsin Edwards blog about uncertainty in science, and climate science
- 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.
- On Thomas Edison and Solar Electric Power
- Skeptical Science
- The Scientific Case for Modern Human-caused Global Warming
- James Hansen and granddaughter Sophie on moving forward with progress on climate
- Mrooijer's Global Temperature Explorer
- Solar Gardens Community Power
- “The Irrelevance of Saturation: Why Carbon Dioxide Matters'' (Bart Levenson)
- AIP's history of global warming science: impacts The American Institute of Physics has a fine history of the science of climate change. This link summarizes the history of impacts of climate change.
- World Weather Attribution
- "Getting to the Energy Future We Want," Dr Steven Chu
- Social Cost of Carbon
- Climate Change Reports By John and Mel Harte
- Klaus Lackner (ASU), Silicon Kingdom Holdings (SKH) Capturing CO2 from air at scale
- Ice and Snow
- Simple box models and climate forcing IMO one of Tamino’s best posts illustrating climate forcing using simple box models
- Climate Communication Hassol, Somerville, Melillo, and Hussin site communicating climate to the public
- Interview with Wally Broecker Interview with Wally Broecker
- Climate model projections versus observations
- `Who to believe on climate change': Simple checks By Bart Verheggen
- Earth System Models
- 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
- The Sunlight Economy
- 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.
- Jacobson WWS literature index
- Wind sled Wind sled: A zero carbon way of exploring ice sheets
- Grid parity map for Solar PV in United States
- Simple models of climate change
- Non-linear feedbacks in climate (discussion of Bloch-Johnson, Pierrehumbert, Abbot paper) Discussion of http://onlinelibrary.wiley.com/wol1/doi/10.1002/2015GL064240/abstract
- Rabett Run Incisive analysis of climate science versus deliberate distraction
- Tell Utilities Solar Won't Be Killed Barry Goldwater, Jr’s campaign to push for solar expansion against monopolistic utilities, as a Republican
- Risk and Well-Being
- Dessler's 6 minute Greenhouse Effect video
- NOAA Annual Greenhouse Gas Index report The annual assessment by the National Oceanic and Atmospheric Administration of the radiative forcing from constituent atmospheric greenhouse gases
- Reanalyses.org
- The Carbon Cycle The Carbon Cycle, monitored by The Carbon Project
- Thriving on Low Carbon
- Sea Change Boston
- MIT's Climate Primer
- Professor Robert Strom's compendium of resources on climate change Truly excellent
- 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.
- `The unchained goddess' 1958 Bell Telephone Science Hour broadcast regarding, among other things, climate change.
- 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.
- David Appell's early climate science
- Tamino's Open Mind Open Mind: A statistical look at climate, its science, and at science denial
- ATTP summarizes all that stuff about Committed Warming from AND THEN THERE’S PHYSICS
Archives
Jan Galkowski
Category Archives: stochastic search
Sampling: Rejection, Reservoir, and Slice
An article by Suilou Huang for catatrophe modeler AIR-WorldWide of Boston about rejection sampling in CAT modeling got me thinking about pulling together some notes about sampling algorithms of various kinds. There are, of course, books written about this subject, … Continue reading
Posted in accept-reject methods, American Statistical Association, Bayesian computational methods, catastrophe modeling, data science, diffusion processes, empirical likelihood, Gibbs Sampling, insurance, Markov Chain Monte Carlo, mathematics, Mathematics and Climate Research Network, maths, Monte Carlo Statistical Methods, multivariate statistics, numerical algorithms, numerical analysis, numerical software, numerics, percolation theory, Python 3 programming language, R statistical programming language, Radford Neal, sampling, slice sampling, spatial statistics, statistics, stochastic algorithms, stochastic search
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Six cases of models
The previous post included an attempt to explain land surface temperatures as estimated by the BEST project using a dynamic linear model including regressions on both quarterly CO2 concentrations and ocean heat content. The idea was to check the explanatory … Continue reading
Posted in AMETSOC, anemic data, Anthropocene, astrophysics, Bayesian, Berkeley Earth Surface Temperature project, BEST, carbon dioxide, climate, climate change, climate data, climate disruption, climate models, dlm package, dynamic linear models, dynamical systems, environment, fossil fuels, geophysics, Giovanni Petris, global warming, greenhouse gases, Hyper Anthropocene, information theoretic statistics, maths, maximum likelihood, meteorology, model comparison, numerical software, Patrizia Campagnoli, Rauch-Tung-Striebel, Sonia Petrone, state-space models, stochastic algorithms, stochastic search, SVD, time series
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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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high dimension Metropolis-Hastings algorithms
If attempting to simulate from a multivariate standard normal distribution in a large dimension, when starting from the mode of the target, i.e., its mean γ, leaving the mode γis extremely unlikely, given the huge drop between the value of the density at the mode γ and at likely realisations Continue reading
Posted in Bayes, Bayesian, Bayesian inversion, boosting, chance, Christian Robert, computation, ensembles, Gibbs Sampling, James Spall, Jerome Friedman, Markov Chain Monte Carlo, mathematics, maths, MCMC, Monte Carlo Statistical Methods, multivariate statistics, numerical software, numerics, optimization, reasonableness, Robert Schapire, SPSA, state-space models, statistics, stochastic algorithms, stochastic search, stochastics, Yoav Freund
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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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reblog: “Tiny Data, Approximate Bayesian Computation and the Socks of Karl Broman”
It’s Rasmus Bååth, in a post and video of which I am very fond: http://www.sumsar.net/blog/2014/10/tiny-data-and-the-socks-of-karl-broman/.
Southern New England Meteorology Conference, 24th October 2015
I attending the 2015 edition of the Southern New England Meteorology Conference in Milton, MA, near the Blue Hill, and its Blue Hill Climatological Observatory, of which I am a member as we as of the American Meteorological Society. I … Continue reading
Posted in Anthropocene, capricious gods, climate, Dan Satterfield, dynamical systems, ensembles, ENSO, environment, floods, forecasting, geophysics, Hyper Anthropocene, information theoretic statistics, mesh models, meteorology, model comparison, NCAR, NOAA, nor'easters, oceanography, probability, science, spatial statistics, state-space models, statistics, stochastic algorithms, stochastic search, stochastics, time series
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On differential localization of tumors using relative concentrations of ctDNA. Part 1.
Like most mammalian tissue, tumors often produce shards of DNA as a byproduct of cell death and fracture. This circulating tumor DNA is being studied as a means of detecting tumors or their resurgence after treatment. (See also a Q&A … Continue reading
Posted in approximate Bayesian computation, Bayesian, Bayesian inversion, cardiovascular system, diffusion, dynamic linear models, eigenanalysis, engineering, forecasting, mathematics, maths, medicine, networks, prediction, spatial statistics, statistics, stochastic algorithms, stochastic search, wave equations
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On the Climate Club
But if the other advanced nations had a stick — a tariff of 4 percent on the imports from countries not in the “climate club” — the cost-benefit calculation for the United States would flip. Not participating in the club … Continue reading
Posted in citizenship, civilization, climate, climate change, climate disruption, climate education, ecology, economics, education, environment, ethics, geophysics, global warming, humanism, investing, investment in wind and solar energy, IPCC, mathematics, mathematics education, maths, meteorology, NASA, NCAR, NOAA, open data, open source scientific software, politics, rationality, reasonableness, risk, science, science education, sociology, state-space models, statistics, stochastic search, stochastics, sustainability, temporal myopia, time series, transparency, Unitarian Universalism, UU Humanists, wind power, zero carbon
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“A vignette on Metropolis” (Christian Robert)
This is a very welcome addition by a master of Bayesian computation, providing a great, brief answer for many of my colleagues who ask, “What’s this MCMC thing about anyway?”
Markov Chain Monte Carlo methods and logistic regression
This post could also be subtitled “Residual deviance isn’t the whole story.” My favorite book on logistic regression is by Dr Joseph Hilbe, Logistic Regression Models, CRC Press, 2009, Chapman & Hill. It is a solidly frequentist text, but its … Continue reading
Posted in Bayes, Bayesian, logistic regression, MCMC, notes, R, statistics, stochastic algorithms, stochastic search
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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
Christian Robert on Alan Turing
Alan Turing Institute. See Professor Robert’s earlier post on Turing, too.
Posted in Bayes, Bayesian, citizenship, education, ethics, history, humanism, mathematics, maths, politics, rationality, reasonableness, statistics, stochastic algorithms, stochastic search, the right to know, Wordpress
Tagged Alan Turing
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engineering and understanding with stable models
Stable distributions or Lévy -stable models is a class of probability distributions which contains the Gaussian, the Cauchy (or Lorentz), and the Lévy distribution. They are parameterized by an which is . Values of of 1 or less give distributions … Continue reading
Posted in approximate Bayesian computation, Bayesian, citizen science, climate, climate change, climate education, differential equations, diffusion processes, ecology, economics, forecasting, geophysics, information theoretic statistics, IPCC, mathematics, mathematics education, maths, meteorology, model comparison, NOAA, oceanography, physics, rationality, reasonableness, risk, science, science education, stochastic search, the right to know
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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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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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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
The dp-means algorithm of Kulis and Jordan in R and Python
dp-means algorithm. Think k-means but with the number of clusters calculated. By John Myles White, in R. (Github link off that page.) By Scott Hendrickson, in Python. (Github link off that page.)
Posted in Bayesian, Gibbs Sampling, JAGS, mathematics, maths, R, statistics, stochastic algorithms, stochastic search
Tagged dp-means
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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
singingbanana does “The Lorenz Machine”
On the power of mathematics, and why 55:45 versus 50:50 matters.
Posted in Bayesian, engineering, mathematics, maths, rationality, reasonableness, risk, stochastic algorithms, stochastic search
Tagged code breaking
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“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
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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“Double Plus Big Data”
Big Data. All the rage. Why? Apart from distributed software folks strutting their stuff, something which is likely to be fleeting, especially when quantum computing comes around, what does it buy anyone? I can see four possibilities, which I consider … Continue reading
“Bayes’ theorem in the 21st century”
Professor Bradley Efron wrote a piece on “Bayes’ theorem in the 21st century” in Science for 7th June 2013 which, as always, offers his measured approach to the frequentist-Bayesian controversy (see B. Efron, “A 250 year argument: Belief, behavior, and the … Continue reading
ecoquant
