Distributed Solar: The Democratizaton of Energy
Blogroll
- Prediction vs Forecasting: Knaub
- Gavin Simpson
- Lenny Smith's CHAOS: A VERY SHORT INTRODUCTION
- Comprehensive Guide to Bayes Rule
- Bob Altemeyer on authoritarianism (via Dan Satterfield)
- Mark Berliner's video lecture "Bayesian mechanistic-statistical modeling with examples in geophysical settings"
- ggplot2 and ggfortify
- The Plastic Pick-Up: Discovering new sources of marine plastic pollution
- SASB
- All about Sankey diagrams
climate change
- Climate model projections versus observations
- Tell Utilities Solar Won't Be Killed
- The great Michael Osborne's latest opinions
- "Getting to the Energy Future We Want," Dr Steven Chu
- Bloomberg interactive graph on “What's warming the world''
- History of discovering Global Warming
- Mrooijer's Global Temperature Explorer
- Updating the Climate Science: What path is the real world following?
- Simple models of climate change
- Skeptical Science
Archives
Jan Galkowski
Category Archives: Bayesian computational methods
“Inferring change points in the spread of COVID-19 reveals the effectiveness of interventions”
J. Dehning et al., Science 369, eabb9789 (2020). DOI: 10.1126/science.abb9789 Source code and data. Note: This is not a classical approach to assessing strength of interventions using either counterfactuals or other kinds of causal inference. Accordingly, the argument for the … Continue reading
Posted in American Association for the Advancement of Science, American Statistical Association, Bayesian, Bayesian computational methods, causal inference, causation, changepoint detection, coronavirus, counterfactuals, COVID-19, epidemiology, SARS-CoV-2, state-space models, statistical series, 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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cdetools package for R: Dalmasso, et al [updated]
Just hit the “arXiv streets”: N. Dalmasso, T. Pospisil, A. B. Lee, R. Izbicki, P. E. Freeman, A. I. Malz, “Conditional Density Estimation Tools in Python and R with applications to photometric redshifts and likelihood-free cosmological inference”, arXiv.org > astro-ph … Continue reading
“Ten Fatal Flaws in Data Analysis” (Charles Kufs)
Professor Kufs has a fun book, Stats with Cats, and a blog. He also has a blog post tiled “Ten Fatal Flaws in Data Analysis” which, in general, I like. But the presentation has some shortcomings, too, which I note … Continue reading
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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