Distributed Solar: The Democratizaton of Energy
Blogroll
- Karl Broman
- Woods Hole Oceanographic Institution (WHOI)
- 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/
- Dominic Cummings blog Chief advisor to the PM, United Kingdom
- 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
- 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.”
- Rasmus Bååth's Research Blog Bayesian statistics and data analysis
- The Mermaid's Tale A conversation about biological complexity and evolution, and the societal aspects of science
- American Statistical Association
- Ted Dunning
- Nadler Strategy, LLC, on sustainability Thinking about business, efficient and effective management, and business value
- 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.
- 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
- Awkward Botany
- Healthy Home Healthy Planet
- Leverhulme Centre for Climate Change Mitigation
- Harvard's Project Implicit
- John Kruschke's "Dong Bayesian data analysis" blog Expanding and enhancing John’s book of same title (now in second edition!)
- 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”
- Mark Berliner's video lecture "Bayesian mechanistic-statistical modeling with examples in geophysical settings"
- Brendon Brewer on Overfitting Important and insightful presentation by Brendon Brewer on overfitting
- 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
- Quotes by Nikola Tesla Quotes by Nikola Tesla, including some of others he greatly liked.
- James' Empty Blog
- 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
- Dr James Spall's SPSA
- Number Cruncher Politics
- Leadership lessons from Lao Tzu
- WEAPONS OF MATH DESTRUCTION Cathy O’Neil’s WEAPONS OF MATH DESTRUCTION,
- ggplot2 and ggfortify Plotting State Space Time Series with ggplot2 and ggfortify
- Team Andrew Weinberg Walking September 8th for the Jimmy Fund!
- Survey Methodology, Prof Ron Fricker http://faculty.nps.edu/rdfricke/
- 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/
- Giant vertical monopolies for energy have stopped making sense
- Comprehensive Guide to Bayes Rule
- "Talking Politics" podcast David Runciman, Helen Thompson
- 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
- GeoEnergy Math Prof Paul Pukite’s Web site devoted to energy derived from geological and geophysical processes and categorized according to its originating source.
- Thaddeus Stevens quotes As I get older, I admire this guy more and more
- 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/.
- 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.
- Tony Seba Solar energy, electric vehicle, energy storage, and business disruption professor and visionary
- John Cook's reasons to use Bayesian inference
- Professor David Draper
- "Perpetual Ocean" from NASA GSFC
- Bob Altemeyer on authoritarianism (via Dan Satterfield) The science behind the GOP civil war
- All about models
- Carl Safina's blog One of the wisest on Earth
climate change
- The net average effect of a warming climate is increased aridity (Professor Steven Sherwood)
- James Hansen and granddaughter Sophie on moving forward with progress on climate
- "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.
- An open letter to Steve Levitt
- Model state level energy policy for New Englad Bob Massie’s proposed energy policy for Massachusetts, an admirable model for energy policy anywhere in New England
- Solar Gardens Community Power
- 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.
- Sir David King David King’s perspective on climate, and the next thousands of years for humanity
- Tamino's Open Mind Open Mind: A statistical look at climate, its science, and at science denial
- The Carbon Cycle The Carbon Cycle, monitored by The Carbon Project
- Jacobson WWS literature index
- MIT's Climate Primer
- Ray Pierrehumbert's site related to "Principles of Planetary Climate" THE book on climate science
- "Climate science is setttled enough"
- `The unchained goddess' 1958 Bell Telephone Science Hour broadcast regarding, among other things, climate change.
- "Warming Slowdown?" (part 2 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. The second part.
- Simple box models and climate forcing IMO one of Tamino’s best posts illustrating climate forcing using simple box models
- Rabett Run Incisive analysis of climate science versus deliberate distraction
- "Lessons of the Little Ice Age" (Farber) From Dan Farber, at LEGAL PLANET
- Updating the Climate Science: What path is the real world following? From Professors Makiko Sato & James Hansen of Columbia University
- “Ways to [try to] slow the Solar Century''
- "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.
- 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.
- Andy Zucker's "Climate Change and Psychology"
- Wind sled Wind sled: A zero carbon way of exploring ice sheets
- RealClimate
- Climate at a glance Current state of the climate, from NOAA
- Klaus Lackner (ASU), Silicon Kingdom Holdings (SKH) Capturing CO2 from air at scale
- Earth System Models
- SolarLove
- Tell Utilities Solar Won't Be Killed Barry Goldwater, Jr’s campaign to push for solar expansion against monopolistic utilities, as a Republican
- Exxon-Mobil statement on UNFCCC COP21
- Berkeley Earth Surface Temperature
- 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.
- Professor Robert Strom's compendium of resources on climate change Truly excellent
- Tuft's Professor Kenneth Lang on the physical chemistry of the Greenhouse Effect
- History of discovering Global Warming From the American Institute of Physics.
- Sea Change Boston
- Climate Change Denying Organizations
- Mrooijer's Global Temperature Explorer
- Thriving on Low Carbon
- 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
- “The discovery of global warming'' (American Institute of Physics)
- Spectra Energy exposed
- Ellenbogen: There is no Such Thing as Wind Turbine Syndrome
- 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
- Anti—Anti-#ClimateEmergency Whether to declare a climate emergency is debatable. But some critics have gone way overboard.
- On Thomas Edison and Solar Electric Power
- Reanalyses.org
- Wally Broecker on climate realism
Archives
Jan Galkowski
Category Archives: Monte Carlo Statistical Methods
Phase Plane plots of COVID-19 deaths with uncertainties
I. Introduction. It’s time to fulfill the promise made in “Phase plane plots of COVID-19 deaths“, a blog post from 2nd May 2020, and produce the same with uncertainty clouds about the functional trajectories(*). To begin, here are some assumptions … Continue reading
Posted in American Statistical Association, Andrew Harvey, anomaly detection, count data regression, COVID-19, dependent data, dlm package, Durbin and Koopman, dynamic linear models, epidemiology, filtering, forecasting, Kalman filter, LaTeX, model-free forecasting, Monte Carlo Statistical Methods, numerical algorithms, numerical linear algebra, population biology, population dynamics, prediction, R, R statistical programming language, regression, statistical learning, stochastic algorithms
Tagged prediction intervals
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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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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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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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A quick note on modeling operational risk from count data
The blog statcompute recently featured a proposal encouraging the use of ordinal models for difficult risk regressions involving count data. This is actually a second installment of a two-part post on this problem, the first dealing with flexibility in count … Continue reading
Posted in American Statistical Association, Bayesian, Bayesian computational methods, count data regression, dichotomising continuous variables, dynamic generalized linear models, Frank Harrell, Frequentist, Generalize Additive Models, generalized linear mixed models, generalized linear models, GLMMs, GLMs, John Kruschke, maximum likelihood, model comparison, Monte Carlo Statistical Methods, multivariate statistics, nonlinear, numerical software, numerics, premature categorization, probit regression, statistical regression, statistics
Tagged dichotomising continuous variables, dichotomizing continuous variables, premature categorization, splines
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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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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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Cory Lesmeister’s treatment of Simson’s Paradox (at “Fear and Loathing in Data Science”)
(Updated 2016-05-08, to provide reference for plateaus of ML functions in vicinity of MLE.) Simpson’s Paradox is one of those phenomena of data which really give Statistics a substance and a role, beyond the roles it inherits from, say, theoretical … Continue reading
Posted in Akaike Information Criterion, approximate Bayesian computation, Bayes, Bayesian, evidence, Frequentist, games of chance, information theoretic statistics, Kalman filter, likelihood-free, mathematics, maths, maximum likelihood, Monte Carlo Statistical Methods, probabilistic programming, rationality, Rauch-Tung-Striebel, Simpson's Paradox, state-space models, statistical dependence, statistics, stochastics
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“Lucky d20” (by Tamino, with my reblogging comments)
Originally posted on Open Mind:
What with talk of killer heat waves, droughts, floods, etc. etc., this blog tends to get pretty serious. When it does, we don’t deal with happy prospects, but with the danger of worldwide catastrophe. But…
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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ecoquant
