667 per centimeter : climate science, quantitative biology, statistics, and energy policy
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Category Archives: V. M. R. Muggeo

Reanalysis of business visits from deployments of a mobile phone app

Posted on 20 February 2020 by ecoquant

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
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    • "Consider a Flat Pond" Invited talk introducing systems thinking, by Jan Galkowski, at First Parish in Needham, UU, via Zoom
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    • 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
    • John Cook's reasons to use Bayesian inference
    • 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.
    • Mike Bloomberg, 2020 He can get progress on climate done, has the means and experts to counter the Trump and Republican digital disinformation machine, and has the experience, knowledge, and depth of experience to achieve and unify.
    • The Keeling Curve: its history History of the Keeling Curve and Charles David Keeling
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    • Brian McGill's Dynamic Ecology blog Quantitative biology with pithy insights regarding applications of statistical methods
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    • 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
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  • climate change

    • 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
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    • Simple box models and climate forcing IMO one of Tamino’s best posts illustrating climate forcing using simple box models
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    • 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.
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    • Anti—Anti-#ClimateEmergency Whether to declare a climate emergency is debatable. But some critics have gone way overboard.
    • "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.
    • Ellenbogen: There is no Such Thing as Wind Turbine Syndrome
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    • "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
    • 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.
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    • The Carbon Cycle The Carbon Cycle, monitored by The Carbon Project
    • "Lessons of the Little Ice Age" (Farber) From Dan Farber, at LEGAL PLANET
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  • Goodreads

  • Kalman filtering and smoothing; dynamic linear models



    Also, see datasets and R examples to accompany this excellent text.





    I have used dlm almost exclusively, except when extreme efficiency was required. Since Jouni Helske's KFAS was rewritten, though, I'm increasingly drawn to it, because the noise sources it supports are more diverse than dlm's. KFAS uses the notation and approaches of Durbin, Koopman, and Harvey.

    ``The real problem is that programmers have spent far too much time worrying about efficiency in the wrong places and at the wrong times; premature optimization is the root of all evil (or at least most of it) in programming.''
    Professor Donald Knuth, 1974
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