Distributed Solar: The Democratizaton of Energy
Blogroll
- Thaddeus Stevens quotes As I get older, I admire this guy more and more
- GeoEnergy Math Prof Paul Pukite’s Web site devoted to energy derived from geological and geophysical processes and categorized according to its originating source.
- The Mermaid's Tale A conversation about biological complexity and evolution, and the societal aspects of science
- 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.
- Rasmus Bååth's Research Blog Bayesian statistics and data analysis
- Dr James Spall's SPSA
- "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.
- South Shore Recycling Cooperative Materials management, technical assistance and networking, town advocacy, public outreach
- Brian McGill's Dynamic Ecology blog Quantitative biology with pithy insights regarding applications of statistical methods
- distributed solar and matching location to need
- Bob Altemeyer on authoritarianism (via Dan Satterfield) The science behind the GOP civil war
- Survey Methodology, Prof Ron Fricker http://faculty.nps.edu/rdfricke/
- WEAPONS OF MATH DESTRUCTION, reviews Reviews of Cathy O’Neil’s new book
- BioPython A collection of Python tools for quantitative Biology
- SASB Sustainability Accounting Standards Board
- ggplot2 and ggfortify Plotting State Space Time Series with ggplot2 and ggfortify
- 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/
- All about Sankey diagrams
- Ted Dunning
- Dollars per BBL: Energy in Transition
- Harvard's Project Implicit
- Quotes by Nikola Tesla Quotes by Nikola Tesla, including some of others he greatly liked.
- James' Empty Blog
- "Consider a Flat Pond" Invited talk introducing systems thinking, by Jan Galkowski, at First Parish in Needham, UU, via Zoom
- Ives and Dakos techniques for regime changes in series
- Flettner Rotor Bruce Yeany introduces the Flettner Rotor and related science
- John Cook's reasons to use Bayesian inference
- American Statistical Association
- International Society for Bayesian Analysis (ISBA)
- Mrooijer's Numbers R 4Us
- Los Alamos Center for Bayesian Methods
- Mertonian norms
- Leverhulme Centre for Climate Change Mitigation
- John Kruschke's "Dong Bayesian data analysis" blog Expanding and enhancing John’s book of same title (now in second edition!)
- AP Statistics: Sampling, by Michael Porinchak Twin City Schools
- Dominic Cummings blog Chief advisor to the PM, United Kingdom
- 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
- Risk and Well-Being
- The Plastic Pick-Up: Discovering new sources of marine plastic pollution
- 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
- 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.
- Karl Broman
- The Keeling Curve: its history History of the Keeling Curve and Charles David Keeling
- What If
- 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
- 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.”
- NCAR AtmosNews
- 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.
- 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.
- London Review of Books
climate change
- Energy payback period for solar panels Considering everything, how long do solar panels have to operate to offset the energy used to produce them?
- "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
- The Green Plate Effect Eli Rabett’s “The Green Plate Effect”
- 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.
- 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.
- Skeptical Science
- Klaus Lackner (ASU), Silicon Kingdom Holdings (SKH) Capturing CO2 from air at scale
- Thriving on Low Carbon
- "Climate science is setttled enough"
- `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.
- 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.
- All Models Are Wrong Dr Tamsin Edwards blog about uncertainty in science, and climate science
- "Lessons of the Little Ice Age" (Farber) From Dan Farber, at LEGAL PLANET
- Transitioning to fully renewable energy Professor Saul Griffiths talks to transitioning the customer journey, from a dependency upon fossil fuels to an electrified future
- The Sunlight Economy
- `The unchained goddess' 1958 Bell Telephone Science Hour broadcast regarding, among other things, climate change.
- James Powell on sampling the climate consensus
- The HUMAN-caused greenhouse effect, in under 5 minutes, by Bill Nye
- Agendaists Eli Rabett’s coining of a phrase
- Earth System Models
- SOLAR PRODUCTION at Westwood Statistical Studios Generation charts for our home in Westwood, MA
- Sea Change Boston
- Rabett Run Incisive analysis of climate science versus deliberate distraction
- Grid parity map for Solar PV in United States
- “The Irrelevance of Saturation: Why Carbon Dioxide Matters'' (Bart Levenson)
- History of discovering Global Warming From the American Institute of Physics.
- Spectra Energy exposed
- Climate Change Reports By John and Mel Harte
- Ricky Rood's “What would happen to climate if we (suddenly) stopped emitting GHGs today?
- 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.
- Tamino's Open Mind Open Mind: A statistical look at climate, its science, and at science denial
- Anti—Anti-#ClimateEmergency Whether to declare a climate emergency is debatable. But some critics have gone way overboard.
- Tuft's Professor Kenneth Lang on the physical chemistry of the Greenhouse Effect
- Climate model projections versus observations
- 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
- Dessler's 6 minute Greenhouse Effect video
- And Then There's Physics
- 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.
- weather blocking patterns
- "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.
- Documenting the Climate Deniarati at work
- Climate Communication Hassol, Somerville, Melillo, and Hussin site communicating climate to the public
- Jacobson WWS literature index
- Non-linear feedbacks in climate (discussion of Bloch-Johnson, Pierrehumbert, Abbot paper) Discussion of http://onlinelibrary.wiley.com/wol1/doi/10.1002/2015GL064240/abstract
- “Ways to [try to] slow the Solar Century''
- Wally Broecker on climate realism
- Berkeley Earth Surface Temperature
- Climate at a glance Current state of the climate, from NOAA
- Simple models of climate change
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”.