### Distributed Solar: The Democratizaton of Energy

### Blogroll

- Awkward Botany
- Beautiful Weeds of New York City
- Healthy Home Healthy Planet
- Charlie Kufs' "Stats With Cats" blog
- Risk and Well-Being
- Prediction vs Forecasting: Knaub
- Hermann Scheer
- American Statistical Association
- Lenny Smith's CHAOS: A VERY SHORT INTRODUCTION
- The Plastic Pick-Up: Discovering new sources of marine plastic pollution

### climate change

- On Thomas Edison and Solar Electric Power
- The beach boondoggle
- Mathematics and Climate Research Network
- Non-linear feedbacks in climate (discussion of Bloch-Johnson, Pierrehumbert, Abbot paper)
- "Impacts of Green New Deal energy plans on grid stability, costs, jobs, health, and climate in 143 countries" (Jacobson, Delucchi, Cameron, et al)
- Climate Communication
- Bloomberg interactive graph on “What's warming the world''
- "Warming Slowdown?" (part 2 of 2)
- Klaus Lackner (ASU), Silicon Kingdom Holdings (SKH)
- Sir David King

### Archives

### Jan Galkowski

# Category Archives: JAGS

## Less evidence for a global warming hiatus, and urging more use of Bayesian model averaging in climate science

(This post has been significantly updated midday 15th February 2018.) I’ve written about the supposed global warming hiatus of 2001-2014 before: “‘Overestimated global warming over the past 20 years’ (Fyfe, Gillett, Zwiers, 2013)”, 28 August 2013 “Warming Slowdown?”, Azimuth, Part … Continue reading

Posted in American Statistical Association, Andrew Parnell, anomaly detection, Anthropocene, Bayesian, Bayesian model averaging, Berkeley Earth Surface Temperature project, BEST, climate change, David Spiegelhalter, dependent data, Dublin, GISTEMP, global warming, Grant Foster, HadCRUT4, hiatus, Hyper Anthropocene, JAGS, Markov Chain Monte Carlo, Martyn Plummer, Mathematics and Climate Research Network, MCMC, model-free forecasting, Niamh Cahill, Significance, statistics, Stefan Rahmstorf, Tamino
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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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## “A vignette on Metropolis” (Christian Robert)

Originally posted on Xi'an's Og:

Over the past week, I wrote a short introduction to the Metropolis-Hastings algorithm, mostly in the style of our Introduction to Monte Carlo with R book, that is, with very little theory and…

## We are trying. And the bitterest result is to have so-called colleagues align themselves with the Koch brothers

I attended a 350.org meeting tonight. One group A group presenting there called “Fighting Against Natural Gas” applauded themselves for assailing Senator Whitehouse of Rhode Island for his supportive position on natural gas pipelines. Now, I am no friend of … Continue reading

Posted in Anthropocene, astrophysics, Boston Ethical Society, bridge to nowhere, carbon dioxide, carbon dioxide sequestration, Carbon Tax, chemistry, citizenship, climate, climate change, climate education, consumption, decentralized electric power generation, demand-side solutions, ecology, economics, energy reduction, engineering, forecasting, fossil fuel divestment, investment in wind and solar energy, IPCC, JAGS, meteorology, methane, model comparison, NASA, natural gas, NCAR, Neill deGrasse Tyson, oceanography, open data, physics, politics, population biology, Principles of Planetary Climate, Python 3, R, rationality, reasonableness, reproducible research, risk, science, science education, Scripps Institution of Oceanography
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## Christian Robert on the amazing Gibbs sampler

Professor Christian Robert remarks on the amazing Gibbs sampler. Implicitly he’s also underscoring the power of properly done Bayesian computational analysis. For here we have a problem with a posterior distribution having two strong modes, so a point estimate, like … Continue reading

## An equation-free introduction to Bayesian inference

By Tomoharu Eguchi from 2008: “An Introduction to Bayesian Statistics Without Using Equations“.

## example of Bayesian inversion

This is based upon my solution of Exercise 2.3, page 18, R. Christensen, W. Johnson, A. Branscum, T. E. Hanson, Bayesian Ideas and Data Analysis, Chapman & Hall, 2011. The purpose is to show how information latent in a set … Continue reading

## Bayesian deconvolution of stick lengths

Consider trying to determine the length of a straight stick. Instead of the measurement errors being clustered about zero, suppose the errors are known to be always positive, that is, no measurement ever underestimates the length of the stick. Such … Continue reading

## The dp-means algorithm of Kulis and Jordan in R and Python

dp-means algorithm. Think k-means but with the number of clusters calculated. By John Myles White, in R. (Github link off that page.) By Scott Hendrickson, in Python. (Github link off that page.)

Posted in Bayesian, Gibbs Sampling, JAGS, mathematics, maths, R, statistics, stochastic algorithms, stochastic search
Tagged dp-means
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## Blind Bayesian recovery of components of residential solid waste tonnage from totals data

This is a sketch of how maths and statistics can do something called blind source separation, meaning to estimate the components of data given only their totals. Here, I use Bayesian techniques for the purpose, sometimes called Bayesian inversion, using … Continue reading

## “The joy and martyrdom of trying to be a Bayesian”

Bayesians have all been there. Some of us don’t depend upon producing publications to assure our pay, so we less have the pressure of pleasing peer reviewers. Nonetheless, it’s all reacting to “What the hell are you doing? I don’t … Continue reading

## How fast is JAGS?

How fast is JAGS?.

## The zero-crossings trick for JAGS: Finding roots stochastically

BUGS has a “zeros trick” (Lund, Jackson, Best, Thomas, Spiegelhalter, 2013, pages 204-206; see also an online illustration) for specifying a new distribution which is not in the standard set. The idea is to couple an invented-for-the-moment Poisson density to … Continue reading

Posted in Bayesian, BUGS, education, forecasting, Gibbs Sampling, JAGS, mathematics, MCMC, probabilistic programming, R, statistics, stochastic search
Tagged error-in-variables problem, optimization, zeros trick
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