Distributed Solar: The Democratizaton of Energy
Blogroll
- Leverhulme Centre for Climate Change Mitigation
- NCAR AtmosNews
- 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”
- "Consider a Flat Pond" Invited talk introducing systems thinking, by Jan Galkowski, at First Parish in Needham, UU, via Zoom
- Harvard's Project Implicit
- What If
- International Society for Bayesian Analysis (ISBA)
- Peter Congdon's Bayesian statistical modeling Peter Congdon’s collection of links pertaining to his several books on Bayesian modeling
- Woods Hole Oceanographic Institution (WHOI)
- James' Empty Blog
- Risk and Well-Being
- WEAPONS OF MATH DESTRUCTION, reviews Reviews of Cathy O’Neil’s new book
- 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.
- ggplot2 and ggfortify Plotting State Space Time Series with ggplot2 and ggfortify
- American Statistical Association
- 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
- Ives and Dakos techniques for regime changes in series
- Number Cruncher Politics
- Musings on Quantitative Paleoecology Quantitative methods and palaeoenvironments.
- distributed solar and matching location to need
- South Shore Recycling Cooperative Materials management, technical assistance and networking, town advocacy, public outreach
- The Plastic Pick-Up: Discovering new sources of marine plastic pollution
- 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/
- 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.”
- The Keeling Curve: its history History of the Keeling Curve and Charles David Keeling
- Mark Berliner's video lecture "Bayesian mechanistic-statistical modeling with examples in geophysical settings"
- Charlie Kufs' "Stats With Cats" blog “You took Statistics 101. Now what?”
- 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.
- OOI Data Nuggets OOI Ocean Data Lab: The Data Nuggets
- All about models
- Earle Wilson
- Bob Altemeyer on authoritarianism (via Dan Satterfield) The science behind the GOP civil war
- 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
- Los Alamos Center for Bayesian Methods
- The Mermaid's Tale A conversation about biological complexity and evolution, and the societal aspects of science
- BioPython A collection of Python tools for quantitative Biology
- SASB Sustainability Accounting Standards Board
- Team Andrew Weinberg Walking September 8th for the Jimmy Fund!
- "Talking Politics" podcast David Runciman, Helen Thompson
- Subsidies for wind and solar versus subsidies for fossil fuels
- Quotes by Nikola Tesla Quotes by Nikola Tesla, including some of others he greatly liked.
- AP Statistics: Sampling, by Michael Porinchak Twin City Schools
- Gavin Simpson
- GeoEnergy Math Prof Paul Pukite’s Web site devoted to energy derived from geological and geophysical processes and categorized according to its originating source.
- Carl Safina's blog One of the wisest on Earth
- 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.
- Dollars per BBL: Energy in Transition
- Gabriel's staircase
- Slice Sampling
- Healthy Home Healthy Planet
climate change
- Simple models of climate change
- Jacobson WWS literature index
- Jacobson WWS literature index
- Ellenbogen: There is no Such Thing as Wind Turbine Syndrome
- On Thomas Edison and Solar Electric Power
- “The discovery of global warming'' (American Institute of Physics)
- 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.
- David Appell's early climate science
- James Powell on sampling the climate consensus
- "Getting to the Energy Future We Want," Dr Steven Chu
- 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
- Warming slowdown discussion
- Klaus Lackner (ASU), Silicon Kingdom Holdings (SKH) Capturing CO2 from air at scale
- Wind sled Wind sled: A zero carbon way of exploring ice sheets
- "Lessons of the Little Ice Age" (Farber) From Dan Farber, at LEGAL PLANET
- `Who to believe on climate change': Simple checks By Bart Verheggen
- Ice and Snow
- Exxon-Mobil statement on UNFCCC COP21
- RealClimate
- Reanalyses.org
- Energy payback period for solar panels Considering everything, how long do solar panels have to operate to offset the energy used to produce them?
- Mrooijer's Global Temperature Explorer
- Tuft's Professor Kenneth Lang on the physical chemistry of the Greenhouse Effect
- “Ways to [try to] slow the Solar Century''
- 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''
- "A field guide to the climate clowns"
- Updating the Climate Science: What path is the real world following? From Professors Makiko Sato & James Hansen of Columbia University
- "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
- Thriving on Low Carbon
- HotWhopper: It's excellent. Global warming and climate change. Eavesdropping on the deniosphere, its weird pseudo-science and crazy conspiracy whoppers.
- 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 Change Denying Organizations
- Social Cost of Carbon
- Sea Change Boston
- Climate Communication Hassol, Somerville, Melillo, and Hussin site communicating climate to the public
- Tell Utilities Solar Won't Be Killed Barry Goldwater, Jr’s campaign to push for solar expansion against monopolistic utilities, as a Republican
- 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.
- Nick Bower's "Scared Scientists"
- Tamino's Open Mind Open Mind: A statistical look at climate, its science, and at science denial
- "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.
- All Models Are Wrong Dr Tamsin Edwards blog about uncertainty in science, and climate science
- `The unchained goddess' 1958 Bell Telephone Science Hour broadcast regarding, among other things, climate change.
- 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.
- Earth System Models
- Agendaists Eli Rabett’s coining of a phrase
- The HUMAN-caused greenhouse effect, in under 5 minutes, by Bill Nye
- Ray Pierrehumbert's site related to "Principles of Planetary Climate" THE book on climate science
- Solar Gardens Community Power
- Simple box models and climate forcing IMO one of Tamino’s best posts illustrating climate forcing using simple box models
Archives
Jan Galkowski
Bayes vs the virial theorem
This entry was posted in Bayesian, mathematics, maths, MCMC, reasonableness, science, statistics. Bookmark the permalink.
ecoquant
Apologies for the name confounding, Ewan!
Hi Jan,
The maximum likelihood, errors-in-variable method with selection of predictor variables by profile likelihood ratios described by Hannart et al is (as they acknowledge) quite an ‘old-fashioned’ statistical technique. There’s nothing ‘wrong’ with it per se, but I imagine due to the availability of fast codes for implementing the equivalent Bayesian model (e.g. R or STAN) many (perhaps most?) statisticians outside geophys would go Bayes. Bayesian model selection (or, for the prediction problem, model averaging) of course requires some care to check for sensitivity of the output to the parameter priors. But I would think it could have a lot of potential here owing to its flexibility: e.g. the errors don’t have to be assumed Normal (perhaps fat-tailed distributions make more sense), and/or the predictor variable set could be expanded to include variables for which one might not have observations at all places / time-points via data augmentation (e.g. http://amstat.tandfonline.com/doi/abs/10.1198/jasa.2011.tm10518?src=recsys ).
cheers, Ewan (not Drew: but i get the confusion from my username which omits the periods in dr.ewan.cameron)
Thanks Drew! I’ll need to dig into that some time soon. (At least I’ll try.) Doesn’t really look too bad … I’m familiar with Polya urns, and stick-breaking processes are the heart of the Bayesian bootstrap which, in my immediate world, finds its way into Bayesian approaches for finite population sampling (Ghosh and Meeden).
I have SO many things to read, meaning study! Doing a lot of writing, too.
On the error in all variables problem, there’s a major paper on the climate science front,
A. Hannart, A. Ribes, and P. Naveau, “Optimal fingerprinting under multiple sources of uncertainty”, GEOPHYSICAL RESEARCH LETTERS, http://dx.doi.org/10.1002/2013GL058653
which I need to give priority.
There’s also one with an intriguing title and abstract, but I don’t know if it’s special or not:
D. Williamson, A. T. Blaker, “Evolving Bayesian Emulators for Structured Chaotic Time Series, with Application to Large Climate Models”, http://dx.doi.org/10.1137/120900915, 2014.
One update to my thoughts on non-parametric error models for semi-parametric Bayesian analyses: one limitation of the Dirichlet Process is that its concentration parameter controls both the ‘spike-iness’ of its realisations *and* their allowed ‘deviation’ from the reference distribution, so it may be worth exploring the more general class of Chinese restaurant processes reviewed thoroughly and explained (at a rather sophisticated level) by Zhou and Carin in “Negative Binomial Process Count and Mixture Modelling”.