### Distributed Solar: The Democratizaton of Energy

### Blogroll

- Gavin Simpson
- Dominic Cummings blog Chief advisor to the PM, United Kingdom
- South Shore Recycling Cooperative Materials management, technical assistance and networking, town advocacy, public outreach
- Karl Broman
- 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”
- Earth Family Beta MIchael Osborne’s blog on Science and the like
- Number Cruncher Politics
- Dollars per BBL: Energy in Transition
- All about Sankey diagrams
- "Perpetual Ocean" from NASA GSFC
- Giant vertical monopolies for energy have stopped making sense
- Professor David Draper
- Ives and Dakos techniques for regime changes in series
- Los Alamos Center for Bayesian Methods
- OOI Data Nuggets OOI Ocean Data Lab: The Data Nuggets
- "Consider a Flat Pond" Invited talk introducing systems thinking, by Jan Galkowski, at First Parish in Needham, UU, via Zoom
- What If
- ggplot2 and ggfortify Plotting State Space Time Series with ggplot2 and ggfortify
- Awkward Botany
- Earth Family Alpha Michael Osborne’s blog (former Executive at Austin Energy, now Chairman of the Electric Utility Commission for Austin, Texas)
- Quotes by Nikola Tesla Quotes by Nikola Tesla, including some of others he greatly liked.
- 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.
- Musings on Quantitative Paleoecology Quantitative methods and palaeoenvironments.
- 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
- AP Statistics: Sampling, by Michael Porinchak Twin City Schools
- "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.
- 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.”
- Woods Hole Oceanographic Institution (WHOI)
- "Talking Politics" podcast David Runciman, Helen Thompson
- American Statistical Association
- Ted Dunning
- Fear and Loathing in Data Science Cory Lesmeister’s savage journey to the heart of Big Data
- Comprehensive Guide to Bayes Rule
- Healthy Home Healthy Planet
- Charlie Kufs' "Stats With Cats" blog “You took Statistics 101. Now what?”
- American Association for the Advancement of Science (AAAS)
- 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
- WEAPONS OF MATH DESTRUCTION Cathy O’Neil’s WEAPONS OF MATH DESTRUCTION,
- Leverhulme Centre for Climate Change Mitigation
- 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
- 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/.
- Tony Seba Solar energy, electric vehicle, energy storage, and business disruption professor and visionary
- Survey Methodology, Prof Ron Fricker http://faculty.nps.edu/rdfricke/
- Beautiful Weeds of New York City
- 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
- 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
- Rasmus Bååth's Research Blog Bayesian statistics and data analysis
- Leadership lessons from Lao Tzu
- Mertonian norms

### climate change

- Wind sled Wind sled: A zero carbon way of exploring ice sheets
- "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.
- Ellenbogen: There is no Such Thing as Wind Turbine Syndrome
- Dessler's 6 minute Greenhouse Effect video
- And Then There's 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.
- The Green Plate Effect Eli Rabett’s “The Green Plate Effect”
- Jacobson WWS literature index
- “The Irrelevance of Saturation: Why Carbon Dioxide Matters'' (Bart Levenson)
- Jacobson WWS literature index
- Simple models of climate change
- "A field guide to the climate clowns"
- Earth System Models
- Social Cost of Carbon
- Climate at a glance Current state of the climate, from NOAA
- “Ways to [try to] slow the Solar Century''
- Andy Zucker's "Climate Change and Psychology"
- Transitioning to fully renewable energy Professor Saul Griffiths talks to transitioning the customer journey, from a dependency upon fossil fuels to an electrified future
- SOLAR PRODUCTION at Westwood Statistical Studios Generation charts for our home in Westwood, MA
- "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
- Energy payback period for solar panels Considering everything, how long do solar panels have to operate to offset the energy used to produce them?
- On Thomas Edison and Solar Electric Power
- RealClimate
- “The discovery of global warming'' (American Institute of Physics)
- An open letter to Steve Levitt
- "Getting to the Energy Future We Want," Dr Steven Chu
- Nick Bower's "Scared Scientists"
- 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
- Climate Communication Hassol, Somerville, Melillo, and Hussin site communicating climate to the public
- Grid parity map for Solar PV in United States
- weather blocking patterns
- 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
- Sea Change Boston
- Climate Change Denying Organizations
- Warming slowdown discussion
- The net average effect of a warming climate is increased aridity (Professor Steven Sherwood)
- "Lessons of the Little Ice Age" (Farber) From Dan Farber, at LEGAL PLANET
- SolarLove
- History of discovering Global Warming From the American Institute of Physics.
- `Who to believe on climate change': Simple checks By Bart Verheggen
- Eli on the spectroscopic basis of atmospheric radiation physical chemistry
- Agendaists Eli Rabett’s coining of a phrase
- Ice and Snow
- Wally Broecker on climate realism
- Exxon-Mobil statement on UNFCCC COP21
- HotWhopper: It's excellent. Global warming and climate change. Eavesdropping on the deniosphere, its weird pseudo-science and crazy conspiracy whoppers.
- Mrooijer's Global Temperature Explorer
- Simple box models and climate forcing IMO one of Tamino’s best posts illustrating climate forcing using simple box models
- Thriving on Low Carbon
- Reanalyses.org

### 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
1 Comment

## 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
1 Comment

## 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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