Distributed Solar: The Democratizaton of Energy
Blogroll
- Dominic Cummings blog Chief advisor to the PM, United Kingdom
- 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.
- Mertonian norms
- distributed solar and matching location to need
- Patagonia founder Yvon Chouinard on how businesses can help our collective environmental mess Patagonia’s Yvon Chouinard set the standard for how a business can mitigate the ravages of capitalism on earth’s environment. At 81 years old, he’s just getting started.
- Fear and Loathing in Data Science Cory Lesmeister’s savage journey to the heart of Big Data
- Nadler Strategy, LLC, on sustainability Thinking about business, efficient and effective management, and business value
- 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”
- Leverhulme Centre for Climate Change Mitigation
- Gabriel's staircase
- BioPython A collection of Python tools for quantitative Biology
- ggplot2 and ggfortify Plotting State Space Time Series with ggplot2 and ggfortify
- "The Expert"
- Thaddeus Stevens quotes As I get older, I admire this guy more and more
- Rasmus Bååth's Research Blog Bayesian statistics and data analysis
- Peter Congdon's Bayesian statistical modeling Peter Congdon’s collection of links pertaining to his several books on Bayesian modeling
- Healthy Home Healthy Planet
- 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/
- "Talking Politics" podcast David Runciman, Helen Thompson
- 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.
- 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.
- 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/
- WEAPONS OF MATH DESTRUCTION Cathy O’Neil’s WEAPONS OF MATH DESTRUCTION,
- Label Noise
- South Shore Recycling Cooperative Materials management, technical assistance and networking, town advocacy, public outreach
- International Society for Bayesian Analysis (ISBA)
- 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
- All about Sankey diagrams
- 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
- 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
- Number Cruncher Politics
- John Cook's reasons to use Bayesian inference
- The Keeling Curve: its history History of the Keeling Curve and Charles David Keeling
- 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.”
- Earle Wilson
- 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
- Slice Sampling
- 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/.
- The Mermaid's Tale A conversation about biological complexity and evolution, and the societal aspects of science
- Ted Dunning
- Subsidies for wind and solar versus subsidies for fossil fuels
- Woods Hole Oceanographic Institution (WHOI)
- Comprehensive Guide to Bayes Rule
- American Statistical Association
- Mrooijer's Numbers R 4Us
- "Consider a Flat Pond" Invited talk introducing systems thinking, by Jan Galkowski, at First Parish in Needham, UU, via Zoom
- James' Empty Blog
- WEAPONS OF MATH DESTRUCTION, reviews Reviews of Cathy O’Neil’s new book
- Ives and Dakos techniques for regime changes in series
climate change
- Grid parity map for Solar PV in United States
- Interview with Wally Broecker Interview with Wally Broecker
- The great Michael Osborne's latest opinions Michael Osborne is a genius operative and champion of solar energy. I have learned never to disregard ANYTHING he says. He is mentor of Karl Ragabo, and the genius instigator of the Texas renewable energy miracle.
- Skeptical Science
- Climate Change Reports By John and Mel Harte
- An open letter to Steve Levitt
- 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.
- "A field guide to the climate clowns"
- Berkeley Earth Surface Temperature
- Mrooijer's Global Temperature Explorer
- Simple models of climate change
- SolarLove
- James Hansen and granddaughter Sophie on moving forward with progress on climate
- Transitioning to fully renewable energy Professor Saul Griffiths talks to transitioning the customer journey, from a dependency upon fossil fuels to an electrified future
- Non-linear feedbacks in climate (discussion of Bloch-Johnson, Pierrehumbert, Abbot paper) Discussion of http://onlinelibrary.wiley.com/wol1/doi/10.1002/2015GL064240/abstract
- Exxon-Mobil statement on UNFCCC COP21
- Energy payback period for solar panels Considering everything, how long do solar panels have to operate to offset the energy used to produce them?
- The Sunlight Economy
- Tell Utilities Solar Won't Be Killed Barry Goldwater, Jr’s campaign to push for solar expansion against monopolistic utilities, as a Republican
- Climate Change Denying Organizations
- RealClimate
- Dessler's 6 minute Greenhouse Effect video
- Professor Robert Strom's compendium of resources on climate change Truly excellent
- 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.
- Ray Pierrehumbert's site related to "Principles of Planetary Climate" THE book on climate science
- “The discovery of global warming'' (American Institute of Physics)
- "Lessons of the Little Ice Age" (Farber) From Dan Farber, at LEGAL PLANET
- 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
- Klaus Lackner (ASU), Silicon Kingdom Holdings (SKH) Capturing CO2 from air at scale
- And Then There's Physics
- “Ways to [try to] slow the Solar Century''
- All Models Are Wrong Dr Tamsin Edwards blog about uncertainty in science, and climate science
- 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.
- Climate model projections versus observations
- `The unchained goddess' 1958 Bell Telephone Science Hour broadcast regarding, among other things, climate change.
- Jacobson WWS literature index
- HotWhopper: It's excellent. Global warming and climate change. Eavesdropping on the deniosphere, its weird pseudo-science and crazy conspiracy whoppers.
- Andy Zucker's "Climate Change and Psychology"
- Wally Broecker on climate realism
- Simple box models and climate forcing IMO one of Tamino’s best posts illustrating climate forcing using simple box models
- Équiterre Equiterre helps build a social movement by encouraging individuals, organizations and governments to make ecological and equitable choices, in a spirit of solidarity.
- History of discovering Global Warming From the American Institute of Physics.
- On Thomas Edison and Solar Electric Power
- MIT's Climate Primer
- Sea Change Boston
- 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
- Bloomberg interactive graph on “What's warming the world''
- Anti—Anti-#ClimateEmergency Whether to declare a climate emergency is debatable. But some critics have gone way overboard.
- Ice and Snow
- Climate at a glance Current state of the climate, from NOAA
Archives
Jan Galkowski
Category Archives: sampling
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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COVID-19 statistics, a caveat : Sources of data matter
There are a number of sources of COVID-19-related demographics, cases, deaths, numbers testing positive, numbers recovered, and numbers testing negative available. Many of these are not consistent with one another. One could hope at least rates would be consistent, but … Continue reading
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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“Ten Fatal Flaws in Data Analysis” (Charles Kufs)
Professor Kufs has a fun book, Stats with Cats, and a blog. He also has a blog post tiled “Ten Fatal Flaws in Data Analysis” which, in general, I like. But the presentation has some shortcomings, too, which I note … Continue reading
On bag bans and sampling plans
Plastic bag bans are all the rage. It’s not the purpose of this post to take a position on the matter. Before you do, however, I’d recommend checking out this: and especially this: (Note: My lovely wife, Claire, presents this … Continue reading
Posted in bag bans, citizen data, citizen science, Commonwealth of Massachusetts, Ecology Action, evidence, Google, Google Earth, Google Maps, goverance, lifestyle changes, microplastics, municipal solid waste, oceans, open data, planning, plastics, politics, pollution, public health, quantitative ecology, R, R statistical programming language, reasonableness, recycling, rhetorical statistics, sampling, sampling networks, statistics, surveys, sustainability
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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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Senn’s `… never having to say you are certain’ guest post from Mayo’s blog
via S. Senn: Being a statistician means never having to say you are certain (Guest Post) See also: E. Cai’s blog post “Applied Statistics Lesson of the Day – The Matched Pairs Experimental Design”, from February 2014 A. Deaton, N. … Continue reading
Posted in abstraction, American Association for the Advancement of Science, American Statistical Association, cancer research, data science, ecology, experimental design, generalized linear mixed models, generalized linear models, Mathematics and Climate Research Network, medicine, sampling, statistics, the right to know
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Eli on “Tom [Karl]’s trick and experimental design“
A very fine post at Eli’s blog for students of statistics, meteorology, and climate (like myself) titled: Tom’s trick and experimental design Excerpt: This and the graph from Menne at the top shows that Karl’s trick is working. Although we … Continue reading
Posted in American Meteorological Association, American Statistical Association, AMETSOC, anomaly detection, climate, climate change, climate data, data science, evidence, experimental design, generalized linear mixed models, GISTEMP, GLMMs, global warming, model comparison, model-free forecasting, reblog, sampling, sampling networks
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“Bigger Isn’t Always Better When It Comes to Data”: Barry Nussbaum
The President’s Corner in the May 2017 issue of Amstat News, the monthly newsletter of the American Statistical Association (“ASA”), features the interesting exposition by environmental statistician and President of the ASA, Barry Nussbaum, called “Bigger isn’t always better when … Continue reading
David Spiegelhalter on `how to spot a dodgy statistic’
In this political season, it’s useful to brush up on rhetorical skills, particularly ones involving numbers and statistics, or what John Allen Paulos called numeracy. Professor David Spiegelhalter has written a guide to some of these tricks. Read the whole … Continue reading
Posted in abstraction, anemic data, Bayes, Bayesian, chance, citizenship, civilization, corruption, Daniel Kahneman, disingenuity, Donald Trump, education, games of chance, ignorance, maths, moral leadership, obfuscating data, open data, perceptions, politics, rationality, reason, reasonableness, rhetoric, risk, sampling, science, sociology, statistics, the right to know
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On Smart Data
One of the things I find surprising, if not astonishing, is that in the rush to embrace Big Data, a lot of learning and statistical technique has been left apparently discarded along the way. I’m hardly the first to point … Continue reading
Posted in Akaike Information Criterion, Bayes, Bayesian, Bayesian inversion, big data, bigmemory package for R, changepoint detection, data science, data streams, dlm package, dynamic generalized linear models, dynamic linear models, dynamical systems, Generalize Additive Models, generalized linear models, information theoretic statistics, Kalman filter, linear algebra, logistic regression, machine learning, Markov Chain Monte Carlo, mathematics, mathematics education, maths, maximum likelihood, MCMC, Monte Carlo Statistical Methods, multivariate statistics, numerical analysis, numerical software, numerics, quantitative biology, quantitative ecology, rationality, reasonableness, sampling, smart data, state-space models, statistical dependence, statistics, the right to know, time series
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“Catching long tail distribution” (Ted Dunning)
One of the best presentations on what can happen if someone takes a naive approach to network data. It also highlights what is, to my mind, the greatly underappreciated t-distribution, which is typically only used in connection with frequentist Student … Continue reading
Going down to the Southern Ocean, by Earle Wilson (on the Scripps R/V Roger Revelle)
(Click on picture to see a larger image, and use your browser Back button to return to reading.) Getting steady data from the Earth’s oceans demands commitment and not a little courage. I could never do what these oceanographers do, … Continue reading
Posted in Alison M Macdonald, anemic data, Antarctica, climate data, complex systems, Earle Wilson, Emily Shuckburgh, engineering, environment, fluid dynamics, geophysics, marine biology, NOAA, oceanic eddies, oceanography, open data, Principles of Planetary Climate, sampling, science, Scripps Institution of Oceanography, thermohaline circulation, waves, WHOI, Woods Hole Oceanographic Institution
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Ah, Hypergeometric!
(“Ah, Hypergeometric!” To be said with the same resignation and acceptance as in “I’ll burn my books–Ah, Mephistopheles!” from Faust.)😉 Dr John Cook, eminent all ’round statistician (with a specialty in biostatistics) and statistical consultant, took up a comment I … Continue reading
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