### Distributed Solar: The Democratizaton of Energy

### Blogroll

- Team Andrew Weinberg Walking September 8th for the Jimmy Fund!
- 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.
- Gabriel's staircase
- Nadler Strategy, LLC, on sustainability Thinking about business, efficient and effective management, and business value
- Ted Dunning
- 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.
- Dominic Cummings blog Chief advisor to the PM, United Kingdom
- "Perpetual Ocean" from NASA GSFC
- 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”
- 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.
- All about models
- Risk and Well-Being
- Harvard's Project Implicit
- Earth Family Beta MIchael Osborne’s blog on Science and the like
- "Consider a Flat Pond" Invited talk introducing systems thinking, by Jan Galkowski, at First Parish in Needham, UU, via Zoom
- 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.”
- Fear and Loathing in Data Science Cory Lesmeister’s savage journey to the heart of Big Data
- Flettner Rotor Bruce Yeany introduces the Flettner Rotor and related science
- Mrooijer's Numbers R 4Us
- Musings on Quantitative Paleoecology Quantitative methods and palaeoenvironments.
- Peter Congdon's Bayesian statistical modeling Peter Congdon’s collection of links pertaining to his several books on Bayesian modeling
- WEAPONS OF MATH DESTRUCTION, reviews Reviews of Cathy O’Neil’s new book
- John Kruschke's "Dong Bayesian data analysis" blog Expanding and enhancing John’s book of same title (now in second edition!)
- 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/.
- Number Cruncher Politics
- 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/
- Thaddeus Stevens quotes As I get older, I admire this guy more and more
- 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
- The Mermaid's Tale A conversation about biological complexity and evolution, and the societal aspects of science
- Leverhulme Centre for Climate Change Mitigation
- BioPython A collection of Python tools for quantitative Biology
- Bob Altemeyer on authoritarianism (via Dan Satterfield) The science behind the GOP civil war
- Quotes by Nikola Tesla Quotes by Nikola Tesla, including some of others he greatly liked.
- 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.
- Los Alamos Center for Bayesian Methods
- 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"
- Comprehensive Guide to Bayes Rule
- OOI Data Nuggets OOI Ocean Data Lab: The Data Nuggets
- Slice Sampling
- AP Statistics: Sampling, by Michael Porinchak Twin City Schools
- South Shore Recycling Cooperative Materials management, technical assistance and networking, town advocacy, public outreach
- The Alliance for Securing Democracy dashboard
- American Association for the Advancement of Science (AAAS)
- Tony Seba Solar energy, electric vehicle, energy storage, and business disruption professor and visionary
- Awkward Botany
- Label Noise
- Gavin Simpson
- Healthy Home Healthy Planet
- Earle Wilson

### climate change

- And Then There's Physics
- David Appell's early climate science
- Exxon-Mobil statement on UNFCCC COP21
- 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.
- All Models Are Wrong Dr Tamsin Edwards blog about uncertainty in science, and climate science
- Wind sled Wind sled: A zero carbon way of exploring ice sheets
- Non-linear feedbacks in climate (discussion of Bloch-Johnson, Pierrehumbert, Abbot paper) Discussion of http://onlinelibrary.wiley.com/wol1/doi/10.1002/2015GL064240/abstract
- Ray Pierrehumbert's site related to "Principles of Planetary Climate" THE book on climate science
- 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.
- James Powell on sampling the climate consensus
- Simple box models and climate forcing IMO one of Tamino’s best posts illustrating climate forcing using simple box models
- `Who to believe on climate change': Simple checks By Bart Verheggen
- 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
- “Ways to [try to] slow the Solar Century''
- 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
- Simple models of climate change
- Eli on the spectroscopic basis of atmospheric radiation physical chemistry
- Tuft's Professor Kenneth Lang on the physical chemistry of the Greenhouse Effect
- 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
- Interview with Wally Broecker Interview with Wally Broecker
- Updating the Climate Science: What path is the real world following? From Professors Makiko Sato & James Hansen of Columbia University
- The net average effect of a warming climate is increased aridity (Professor Steven Sherwood)
- Climate model projections versus observations
- Climate at a glance Current state of the climate, from NOAA
- Warming slowdown discussion
- The HUMAN-caused greenhouse effect, in under 5 minutes, by Bill Nye
- Spectra Energy exposed
- Ricky Rood's “What would happen to climate if we (suddenly) stopped emitting GHGs today?
- World Weather Attribution
- Équiterre Equiterre helps build a social movement by encouraging individuals, organizations and governments to make ecological and equitable choices, in a spirit of solidarity.
- Tamino's Open Mind Open Mind: A statistical look at climate, its science, and at science denial
- "A field guide to the climate clowns"
- weather blocking patterns
- Grid parity map for Solar PV in United States
- SolarLove
- On Thomas Edison and Solar Electric Power
- 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.
- RealClimate
- "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.
- Reanalyses.org
- Dessler's 6 minute Greenhouse Effect video
- Climate Communication Hassol, Somerville, Melillo, and Hussin site communicating climate to the public
- Wally Broecker on climate realism
- "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.)
- Bloomberg interactive graph on “What's warming the world''
- Sir David King David King’s perspective on climate, and the next thousands of years for humanity
- Nick Bower's "Scared Scientists"
- Jacobson WWS literature index
- The Carbon Cycle The Carbon Cycle, monitored by The Carbon Project
- "Getting to the Energy Future We Want," Dr Steven Chu

### Archives

### Jan Galkowski

# Category Archives: R statistical programming language

## 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

## On odds of storms, and extreme precipitation

People talk about “thousand year storms”. Rather than being a storm having a recurrence time of once in a thousand years, these are storms which have a 0.001 chance per year of occurring. Storms aren’t the only weather events of … Continue reading

Posted in American Meteorological Association, American Statistical Association, AMETSOC, catastrophe modeling, climate disruption, climate economics, climate education, ecopragmatism, evidence, extreme events, extreme value distribution, flooding, floods, games of chance, global warming, global weirding, insurance, meteorological models, meteorology, R, R statistical programming language, real estate values, risk, Risky Business, riverine flooding, science, Significance
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## Macros in R

via Macros in R See also: The gtools package of R which enables these. There’s a description and motivation beginninng on page 11 of an (old: 2001) R News issue. They have been around a long time, but I haven’t … Continue reading

Posted in macros, R, R statistical programming language
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## Procrustes tangent distance is better than SNCD

I’ve written two posts here on using a Symmetrized Normalized Compression Divergence or SNCD for comparing time series. One introduced the SNCD and described its relationship to compression distance, and the other applied the SNCD to clustering days at a … Continue reading

Posted in data science, dependent data, descriptive statistics, divergence measures, hydrology, Ian Dryden, information theoretic statistics, J.T.Kent, Kanti Mardia, non-parametric statistics, normalized compression divergence, quantitative ecology, R statistical programming language, spatial statistics, statistical series, time series
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## On bag bans and sampling plans

Plastic bag bans are all the rage. It’s not the purpose of this post to take a position on the matter. Before you do, however, I’d recommend checking out this: and especially this: (Note: My lovely wife, Claire, presents this … Continue reading

Posted in bag bans, citizen data, citizen science, Commonwealth of Massachusetts, Ecology Action, evidence, Google, Google Earth, Google Maps, goverance, lifestyle changes, microplastics, municipal solid waste, oceans, open data, planning, plastics, politics, pollution, public health, quantitative ecology, R, R statistical programming language, reasonableness, recycling, rhetorical statistics, sampling, sampling networks, statistics, surveys, sustainability
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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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