Distributed Solar: The Democratizaton of Energy
Blogroll
- International Society for Bayesian Analysis (ISBA)
- James' Empty Blog
- Woods Hole Oceanographic Institution (WHOI)
- The Mermaid's Tale A conversation about biological complexity and evolution, and the societal aspects of science
- Dr James Spall's SPSA
- AP Statistics: Sampling, by Michael Porinchak Twin City Schools
- 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
- Professor David Draper
- 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.
- American Statistical Association
- SASB Sustainability Accounting Standards Board
- 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/
- Brendon Brewer on Overfitting Important and insightful presentation by Brendon Brewer on overfitting
- The Keeling Curve: its history History of the Keeling Curve and Charles David Keeling
- 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
- All about Sankey diagrams
- The Plastic Pick-Up: Discovering new sources of marine plastic pollution
- 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
- Nadler Strategy, LLC, on sustainability Thinking about business, efficient and effective management, and business value
- Earth Family Beta MIchael Osborne’s blog on Science and the like
- 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.
- Flettner Rotor Bruce Yeany introduces the Flettner Rotor and related science
- Musings on Quantitative Paleoecology Quantitative methods and palaeoenvironments.
- 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”
- distributed solar and matching location to need
- The Alliance for Securing Democracy dashboard
- Number Cruncher Politics
- Brian McGill's Dynamic Ecology blog Quantitative biology with pithy insights regarding applications of statistical methods
- 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/.
- American Association for the Advancement of Science (AAAS)
- OOI Data Nuggets OOI Ocean Data Lab: The Data Nuggets
- 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.
- Leadership lessons from Lao Tzu
- ggplot2 and ggfortify Plotting State Space Time Series with ggplot2 and ggfortify
- 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
- All about models
- 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
- 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.
- 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/
- Leverhulme Centre for Climate Change Mitigation
- Gabriel's staircase
- "The Expert"
- NCAR AtmosNews
- WEAPONS OF MATH DESTRUCTION, reviews Reviews of Cathy O’Neil’s new book
- 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.”
- 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.
- Slice Sampling
- Label Noise
- Charlie Kufs' "Stats With Cats" blog “You took Statistics 101. Now what?”
- London Review of Books
climate change
- Équiterre Equiterre helps build a social movement by encouraging individuals, organizations and governments to make ecological and equitable choices, in a spirit of solidarity.
- The Green Plate Effect Eli Rabett’s “The Green Plate Effect”
- Bloomberg interactive graph on “What's warming the world''
- “The discovery of global warming'' (American Institute of Physics)
- The Scientific Case for Modern Human-caused Global Warming
- Climate impacts on retail and supply chains
- Ricky Rood's “What would happen to climate if we (suddenly) stopped emitting GHGs today?
- RealClimate
- The Carbon Cycle The Carbon Cycle, monitored by The Carbon Project
- `Who to believe on climate change': Simple checks By Bart Verheggen
- HotWhopper: It's excellent. Global warming and climate change. Eavesdropping on the deniosphere, its weird pseudo-science and crazy conspiracy whoppers.
- Ellenbogen: There is no Such Thing as Wind Turbine Syndrome
- Steve Easterbrook's excellent climate blog: See his "The Internet: Saving Civilization or Trashing the Planet?" for example Heavy on data and computation, Easterbrook is a CS prof at UToronto, but is clearly familiar with climate science. I like his “The Internet: Saving Civilization or Trashing the Planet” very much.
- "Lessons of the Little Ice Age" (Farber) From Dan Farber, at LEGAL PLANET
- Thriving on Low Carbon
- Klaus Lackner (ASU), Silicon Kingdom Holdings (SKH) Capturing CO2 from air at scale
- Ray Pierrehumbert's site related to "Principles of Planetary Climate" THE book on climate science
- Tell Utilities Solar Won't Be Killed Barry Goldwater, Jr’s campaign to push for solar expansion against monopolistic utilities, as a Republican
- "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
- History of discovering Global Warming From the American Institute of Physics.
- 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.
- The Sunlight Economy
- World Weather Attribution
- Grid parity map for Solar PV in United States
- Simple box models and climate forcing IMO one of Tamino’s best posts illustrating climate forcing using simple box models
- “The Irrelevance of Saturation: Why Carbon Dioxide Matters'' (Bart Levenson)
- "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.)
- David Appell's early climate science
- Tamino's Open Mind Open Mind: A statistical look at climate, its science, and at science denial
- 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
- Exxon-Mobil statement on UNFCCC COP21
- 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
- And Then There's Physics
- 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
- 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
- Climate Communication Hassol, Somerville, Melillo, and Hussin site communicating climate to the public
- Spectra Energy exposed
- James Hansen and granddaughter Sophie on moving forward with progress on climate
- Agendaists Eli Rabett’s coining of a phrase
- Simple models of climate change
- Jacobson WWS literature index
- Mrooijer's Global Temperature Explorer
- "Warming Slowdown?" (part 1 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. In two parts.
- 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.
- ATTP summarizes all that stuff about Committed Warming from AND THEN THERE’S PHYSICS
- Andy Zucker's "Climate Change and Psychology"
- Professor Robert Strom's compendium of resources on climate change Truly excellent
- SolarLove
- 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.
Archives
Jan Galkowski
Category Archives: MCMC
Less evidence for a global warming hiatus, and urging more use of Bayesian model averaging in climate science
(This post has been significantly updated midday 15th February 2018.) I’ve written about the supposed global warming hiatus of 2001-2014 before: “‘Overestimated global warming over the past 20 years’ (Fyfe, Gillett, Zwiers, 2013)”, 28 August 2013 “Warming Slowdown?”, Azimuth, Part … Continue reading
Posted in American Statistical Association, Andrew Parnell, anomaly detection, Anthropocene, Bayesian, Bayesian model averaging, Berkeley Earth Surface Temperature project, BEST, climate change, David Spiegelhalter, dependent data, Dublin, GISTEMP, global warming, Grant Foster, HadCRUT4, hiatus, Hyper Anthropocene, JAGS, Markov Chain Monte Carlo, Martyn Plummer, Mathematics and Climate Research Network, MCMC, model-free forecasting, Niamh Cahill, Significance, statistics, Stefan Rahmstorf, Tamino
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Repaired R code for Markov spatial simulation of hurricane tracks from historical trajectories
(Slight update, 28th June 2020.) I’m currently studying random walk and diffusion processes and their connections with random fields. I’m interested in this because at the core of dynamic linear models, Kalman filters, and state-space methods there is a random … Continue reading
Posted in American Meteorological Association, American Statistical Association, AMETSOC, Arthur Charpentier, atmosphere, diffusion, diffusion processes, dynamic linear models, dynamical systems, environment, geophysics, hurricanes, Kalman filter, Kerry Emanuel, Lévy flights, Lorenz, Markov chain random fields, mathematics, mathematics education, maths, MCMC, mesh models, meteorological models, meteorology, model-free forecasting, Monte Carlo Statistical Methods, numerical analysis, numerical software, oceanography, open data, open source scientific software, physics, random walk processes, random walks, science, spatial statistics, state-space models, statistical dependence, statistics, stochastic algorithms, stochastics, time series
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Newt Gingrich and Van Jones. Right on.
It’s the thing. And it addresses how media and people forget about the actual statistics, and focus on the White Hot Bright Light. A study by Gelman, Fagan, and Kiss A study by Freyer A counterpoint to the Freyer study … Continue reading
Posted in American Statistical Association, Bayes, Bayesian, citizen science, criminal justice, Daniel Kahneman, ethics, evidence, fear uncertainty and doubt, humanism, Lives Matter, logistic regression, Markov Chain Monte Carlo, MCMC, organizational failures, population biology, rationality, reasonableness, risk, statistics, Susan Jacoby, 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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p-values and hypothesis tests: the Bayesian(s) rule
The American Statistical Association of which I am a longtime member issued an important statement today which will hopefully move statistical practice in engineering and especially in the sciences away from the misleading practice of using p-values and hypothesis tests. … Continue reading
Posted in approximate Bayesian computation, arXiv, Bayes, Bayesian, Bayesian inversion, bollocks, Christian Robert, climate, complex systems, data science, Frequentist, information theoretic statistics, likelihood-free, Markov Chain Monte Carlo, MCMC, Monte Carlo Statistical Methods, population biology, rationality, reasonableness, science, scientific publishing, statistical dependence, statistics, stochastics, Student t distribution
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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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R and “big data”
On 2nd November 2015, Wes McKinney, the developer of the highly useful Python pandas module (and other things, including books), wrote an amusing blog post, “The problem with the data science language wars“. I by no means disagree with him. … Continue reading
Sea Surface Anomalies
(Hat tip to Susan Stone.) The graphic below shows sea surface temperature anomalies relative to the 1971-2000 baseline First data are courtesy of the Climate Reanalyzer, a joint project of the Climate Change Institute at the University of Maine, and … Continue reading
Posted in Anthropocene, carbon dioxide, climate, climate change, climate disruption, climate education, differential equations, diffusion processes, dynamical systems, ecology, ENSO, environment, forecasting, geophysics, global warming, Hyper Anthropocene, IPCC, mathematics, MCMC, NASA, NCAR, NOAA, oceanography, open data, physics, Principles of Planetary Climate, rationality, reasonableness, risk, science, science education, sea level rise, statistics, sustainability, the right to know, time series, transparency
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“… the most patronizing start to an answer I have ever received …”
Professor Christian Robert tries to help out a student of MCMC on Cross Validated and earns the comment that his help had “the most patronizing start to an answer I have ever received“. I learned a new term: primitivus petitor.
“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?”
“Unbiased Bayes for Big Data: Path of partial posteriors” (Christian Robert)
Unbiased Bayes for Big Data: Path of partial posteriors.
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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Bayesian change-point analysis for global temperatures, 1850-2010
Professor Peter Congdon reports on two Bayesian models for global temperature shifts in his textbook, Applied Bayesian Modelling, as “Example 6.12: Global temperatures, 1850-2010”, on pages 252-253. A direct link is available online. The first is apparently original with Congdon, … Continue reading
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
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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Liddell and Kruschke, on conditional logistic Bayesian estimation
(“Ostracism and fines in a public goods game with accidental contributions: The importance of punishment type”) An overview. The article
Posted in Bayes, Bayesian, biology, citizenship, civilization, compassion, ecology, economics, ethics, humanism, investing, MCMC, politics, rationality, reasonableness, risk, sociology, statistics
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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
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?.
Sea Level Rise, after Church and White (2006)
Modeling done with a Bayesian Rauch-Tung-Striebel algorithm, estimating priors of variance for observations and state by using a stationary bootstrap for the series using Politis and Romano algorithm. Updated, 30th September 2021 Zhu, Yingli, Gary T. Mitchum, Kara S. Doran, … Continue reading
Posted in Bayesian, carbon dioxide, civilization, climate, climate education, conservation, consumption, ecology, economics, education, efficiency, energy, energy reduction, engineering, environment, forecasting, geoengineering, geophysics, humanism, MCMC, meteorology, oceanography, optimization, physics, politics, rationality, reasonableness, science, statistics
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Bayes vs the virial theorem
Bayes vs the virial theorem.
Posted in Bayesian, mathematics, maths, MCMC, reasonableness, science, statistics
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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
