Category Archives: stochastic algorithms

Baseload is an intellectual crutch for engineers and utility managers who cannot think dynamically

This is an awesome presentation by Professor Joshua Pearce of Michigan Technological University. (h/t Peter Sinclair’s Climate Denial Crock of the Week) The same idea, that “baseload is a shortcut for engineers who can’t think dynamically”, was similar in the … Continue reading

Posted in American Solar Energy Society, an ignorant American public, Bloomberg Green, Bloomberg New Energy Finance, bridge to somewhere, CleanTechnica, control theory, controls theory, decentralized electric power generation, decentralized energy, differential equations, dynamic linear models, dynamical systems, electrical energy engineering, electrical energy storage, electricity, Kalman filter, optimization, photovoltaics, rate of return regulation, solar domination, solar energy, solar revolution, stochastic algorithms, utility company death spiral, wind energy, wind power, zero carbon | Tagged , , , , , , , | Leave a comment

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 | Leave a comment

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

“Applications of Deep Learning to ocean data inference and subgrid parameterization”

This is another nail in the coffin of the claim I heard at last year’s Lorenz-Charney Symposium at MIT that machine learning methods would not make a serious contribution to advancements in the geophysical sciences. T. Bolton, L. Zanna, “Applications … Continue reading

Posted in American Meteorological Association, American Statistical Association, artificial intelligence, Azimuth Project, deep learning, deep recurrent neural networks, dynamical systems, geophysics, machine learning, Mathematics and Climate Research Network, National Center for Atmospheric Research, oceanography, oceans, science, stochastic algorithms | Leave a comment

The Johnson-Lindenstrauss Lemma, and the paradoxical power of random linear operators. Part 1.

Updated, 2018-12-04 I’ll be discussing the ramifications of: William B. Johnson and Joram Lindenstrauss, “Extensions of Lipschitz mappings into a Hilbert space, Contemporary Mathematics, 26:189–206, 1984. for several posts here. Some introduction and links to proofs and explications will be … Continue reading

Posted in clustering, data science, dimension reduction, information theoretic statistics, Johnson-Lindenstrauss Lemma, k-NN, Locality Sensitive Hashing, mathematics, maths, multivariate statistics, non-parametric model, numerical algorithms, numerical linear algebra, point pattern analysis, random projections, recommender systems, science, stochastic algorithms, stochastics, subspace projection methods | 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 | Leave a comment

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

A model of an electrical grid: A vision

Many people seem to view the electrical grid of the future being much like the present one. I think a lot about networks, because of my job. And I especially think a lot about network topologies, although primarily concerning the … Continue reading

Posted in abstraction, American Meteorological Association, anomaly detection, Anthropocene, Bloomberg New Energy Finance, BNEF, Boston, bridge to somewhere, Buckminster Fuller, Canettes Blues Band, clean disruption, climate business, climate economics, complex systems, corporate supply chains, decentralized electric power generation, decentralized energy, demand-side solutions, differential equations, distributed generation, efficiency, EIA, electricity, electricity markets, energy, energy reduction, energy storage, energy utilities, engineering, extended supply chains, green tech, grid defection, Hermann Scheer, Hyper Anthropocene, investment in wind and solar energy, ISO-NE, Kalman filter, kriging, Lawrence Berkeley National Laboratory, leaving fossil fuels in the ground, Lenny Smith, local generation, marginal energy sources, Massachusetts Clean Energy Center, Mathematics and Climate Research Network, mesh models, meteorology, microgrids, networks, New England, New York State, open data, organizational failures, pipelines, planning, prediction markets, public utility commissions, PUCs, rate of return regulation, rationality, reason, reasonableness, regime shifts, regulatory capture, resiliency, risk, Sankey diagram, smart data, solar domination, solar energy, solar power, Spaceship Earth, spatial statistics, state-space models, statistical dependence, statistics, stochastic algorithms, stochastics, stranded assets, supply chains, sustainability, the energy of the people, the green century, the value of financial assets, thermodynamics, time series, Tony Seba, utility company death spiral, wave equations, wind energy, wind power, zero carbon | Leave a comment

“Stochastic Parameterization: Towards a new view of weather and climate models”

Judith Berner, Ulrich Achatz, Lauriane Batté, Lisa Bengtsson, Alvaro De La Cámara, Hannah M. Christensen, Matteo Colangeli, Danielle R. B. Coleman, Daan Crommelin, Stamen I. Dolaptchiev, Christian L.E. Franzke, Petra Friederichs, Peter Imkeller, Heikki Järvinen, Stephan Juricke, Vassili Kitsios, François … Continue reading

Posted in biology, climate models, complex systems, convergent cross-mapping, data science, dynamical systems, ecology, Ethan Deyle, Floris Takens, George Sughihara, Hao Ye, likelihood-free, Lorenz, mathematics, meteorological models, model-free forecasting, physics, population biology, population dynamics, quantitative biology, quantitative ecology, Scripps Institution of Oceanography, state-space models, statistical dependence, statistics, stochastic algorithms, stochastic search, stochastics, Takens embedding theorem, time series, Victor Brovkin | 4 Comments

JASA demands code and data be supplied as a condition of publication

The Journal of the American Statistical Association (“JASA”) has announced in this month’s Amstat News that effective 1st September 2016 “… will require code and data as a minimum standard for reproducibility of statistical scientific research.” Trends were heading this … Continue reading

Posted in American Association for the Advancement of Science, American Statistical Association, citizen science, engineering, ethics, evidence, new forms of scientific peer review, numerical software, planning, rationality, reasonableness, resiliency, science, statistics, stochastic algorithms, testing, the right to know | Leave a comment

France, and Mathematics

Cédric Villani, does Mathematics. “Problems worthy of attack, prove their worth by hitting back.” — Piet Hein

Posted in abstraction, Google, mathematics, mathematics education, maths, networks, Pagerank, percolation theory, point pattern analysis, probability, rationality, reasonableness, stochastic algorithms | Leave a comment

Six cases of models

The previous post included an attempt to explain land surface temperatures as estimated by the BEST project using a dynamic linear model including regressions on both quarterly CO2 concentrations and ocean heat content. The idea was to check the explanatory … Continue reading

Posted in AMETSOC, anemic data, Anthropocene, astrophysics, Bayesian, Berkeley Earth Surface Temperature project, BEST, carbon dioxide, climate, climate change, climate data, climate disruption, climate models, dlm package, dynamic linear models, dynamical systems, environment, fossil fuels, geophysics, Giovanni Petris, global warming, greenhouse gases, Hyper Anthropocene, information theoretic statistics, maths, maximum likelihood, meteorology, model comparison, numerical software, Patrizia Campagnoli, Rauch-Tung-Striebel, Sonia Petrone, state-space models, stochastic algorithms, stochastic search, SVD, time series | 1 Comment

“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…

Posted in Bayes, Bayesian, card decks, card draws, card games, chance, D&D, Dungeons and Dragons, games of chance, mathematics, maths, Monte Carlo Statistical Methods, probability, statistical dependence, statistics, stochastic algorithms, stochastics, Wizards of the Coast | Leave a comment

Of my favorite things …

(Clarifying language added 4 Apr 2016, 12:26 EDT.) I just watched an episode from the last season of Star Trek: The Next Generation entitled “Force of Nature.” As anyone who pays the least attention to this blog knows, opposing human … Continue reading

Posted in Anthropocene, bridge to somewhere, bucket list, Buckminster Fuller, Carl Sagan, climate, climate change, climate disruption, climate education, compassion, data science, Earle Wilson, ecology, Ecology Action, environment, evolution, geophysics, George Sughihara, global warming, Hyper Anthropocene, life purpose, mathematics, mathematics education, maths, numerical analysis, optimization, philosophy, physical materialism, physics, population biology, population dynamics, proud dad, quantitative biology, quantitative ecology, rationality, reasonableness, science, sociology, statistics, stochastic algorithms | 5 Comments

“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 | Leave a comment

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 | Leave a comment

Generating supports for classification rules in black box regression models

Inspired by the extensive and excellent work in approximate Bayesian computation (see also), especially that done by Professors Christian Robert and colleagues (see also), and Professor Simon Wood (see also), it occurred to me that the complaints regarding lack of … Continue reading

Posted in approximate Bayesian computation, Bayes, Bayesian, Bayesian inversion, generalized linear models, machine learning, numerical analysis, numerical software, probabilistic programming, rationality, reasonableness, state-space models, statistics, stochastic algorithms, stochastic search, stochastics, support of black boxes | Leave a comment

All I do is complain, complain …

I was reviewing a presentation given as part of a short course in the machine learning genre today, and happened across the following two bullets, under the heading “Strictly Stationary Processes”: Predicting a time series is possible if and only … Continue reading

Posted in bifurcations, chaos, citizen science, convergent cross-mapping, dynamic linear models, dynamical systems, engineering, Floris Takens, generalized linear models, geophysics, George Sughihara, ignorance, Lenny Smith, Lorenz, mathematics, maths, meteorology, prediction, probability, rationality, reasonableness, statistics, stochastic algorithms, stochastic search, stochastics, Takens embedding theorem, the right to know, time series | 1 Comment

Thoughts on “Regime Shift?”

John Baez at The Azimuth Project opened a discussion on the recent paper by Reid, et al Philip C. Reid et al, Global impacts of the 1980s regime shift on the Earth’s climate and systems, Global Change Biology, 2015. I … Continue reading

Posted in Bayesian, changepoint detection, climate change, climate disruption, climate models, dynamic linear models, ecology, ensembles, environment, global warming, population biology, Rauch-Tung-Striebel, regime shifts, state-space models, stochastic algorithms, time series | Leave a comment

reblog: “Tiny Data, Approximate Bayesian Computation and the Socks of Karl Broman”

It’s Rasmus Bååth, in a post and video of which I am very fond: http://www.sumsar.net/blog/2014/10/tiny-data-and-the-socks-of-karl-broman/.

Posted in approximate Bayesian computation, Bayesian, Bayesian inversion, empirical likelihood, evidence, likelihood-free, probability, rationality, reasonableness, statistics, stochastic algorithms, stochastic search | 1 Comment

What the future of energy everywhere looks like

What will the energy landscape look like after utility companies are either dead, dying, or revert to a tiny portion of their territory? Silicon Valley CCE Partnership gives us all a clue. It’s been described in the San Francisco Chronicle, … Continue reading

Posted in adaptation, Anthropocene, capricious gods, chance, citizenship, civilization, clean disruption, conservation, consumption, decentralized electric power generation, decentralized energy, demand-side solutions, destructive economic development, dynamical systems, economics, efficiency, energy, energy reduction, energy utilities, engineering, environment, ethics, forecasting, fossil fuel divestment, geophysics, global warming, Hyper Anthropocene, investment in wind and solar energy, living shorelines, mesh models, meteorology, microgrids, mitigation, obfuscating data, oceanography, physical materialism, physics, pipelines, planning, politics, prediction, probabilistic programming, public utility commissions, PUCs, quantum, reasonableness, reproducible research, risk, Sankey diagram, science, sea level rise, selfishness, solar energy, solar power, SolarPV.tv, Spaceship Earth, statistics, stochastic algorithms, stochastics, Svante Arrhenius, taxes, temporal myopia, the right to know, the value of financial assets, transparency, UU Humanists, WHOI, wind energy, wind power, zero carbon | Leave a comment

Southern New England Meteorology Conference, 24th October 2015

I attending the 2015 edition of the Southern New England Meteorology Conference in Milton, MA, near the Blue Hill, and its Blue Hill Climatological Observatory, of which I am a member as we as of the American Meteorological Society. I … Continue reading

Posted in Anthropocene, capricious gods, climate, Dan Satterfield, dynamical systems, ensembles, ENSO, environment, floods, forecasting, geophysics, Hyper Anthropocene, information theoretic statistics, mesh models, meteorology, model comparison, NCAR, NOAA, nor'easters, oceanography, probability, science, spatial statistics, state-space models, statistics, stochastic algorithms, stochastic search, stochastics, time series | 1 Comment

On differential localization of tumors using relative concentrations of ctDNA. Part 1.

Like most mammalian tissue, tumors often produce shards of DNA as a byproduct of cell death and fracture. This circulating tumor DNA is being studied as a means of detecting tumors or their resurgence after treatment. (See also a Q&A … Continue reading

Posted in approximate Bayesian computation, Bayesian, Bayesian inversion, cardiovascular system, diffusion, dynamic linear models, eigenanalysis, engineering, forecasting, mathematics, maths, medicine, networks, prediction, spatial statistics, statistics, stochastic algorithms, stochastic search, wave equations | 3 Comments

On Changing Things

You never change things by fighting the existing reality. To change something, build a new model that makes the existing model obsolete. That’s from Buckminster Fuller, a fellow Unitarian.

Posted in adaptation, Anthropocene, bifurcations, bridge to nowhere, Buckminster Fuller, Cauchy distribution, clean disruption, climate disruption, demand-side solutions, destructive economic development, Disney, dynamic linear models, dynamical systems, Epcot, exponential growth, fossil fuel divestment, global warming, Hyper Anthropocene, physical materialism, planning, rationality, reasonableness, Spaceship Earth, stochastic algorithms | Leave a comment

Your future: Antarctica, in detail

Climate and geophysical accuracy demands fine modeling grids, and very large supercomputers. The best and biggest supercomputers have not been available for climate work, until recently. Watch how results differ if fine meshes and big supercomputers are used. Why haven’t … Continue reading

Posted in Antarctica, Anthropocene, bridge to nowhere, climate, climate change, climate disruption, climate zombies, disingenuity, ecology, ensembles, forecasting, geophysics, global warming, Hyper Anthropocene, ignorance, IPCC, Lawrence Berkeley National Laboratory, LBNL, living shorelines, mathematics, mathematics education, maths, mesh models, meteorology, multivariate statistics, numerical software, optimization, physics, rationality, reasonableness, risk, science, science education, sea level rise, spatial statistics, state-space models, statistics, stochastic algorithms, stochastics, supercomputers, temporal myopia, the right to know, thermodynamics, time series, University of California Berkeley, WAIS | Leave a comment

“The Bayesian Second Law of Thermodynamics” (Sean Carroll, and collaborators)

http://www.preposterousuniverse.com/blog/2015/08/11/the-bayesian-second-law-of-thermodynamics/ See also.

Posted in approximate Bayesian computation, Bayesian, bifurcations, Boltzmann, capricious gods, dynamical systems, ensembles, games of chance, Gibbs Sampling, information theoretic statistics, Josiah Willard Gibbs, mathematics, maths, physics, probability, rationality, reasonableness, science, statistics, stochastic algorithms, stochastics, thermodynamics, Wordpress | Leave a comment

Comprehensive and compact tutorial on Petris’ DLM package in R; with an update about Helske’s KFAS

A blogger named Lalas produced on Quantitative Thoughts a very comprehensive and compact tutorial on the R package dlm by Petris. I use dlm a lot. Unfortunately, Lalas does not give details on how the SVD is used. They do … Continue reading

Posted in Bayes, Bayesian, dynamic linear models, dynamical systems, forecasting, Kalman filter, mathematics, maths, multivariate statistics, numerical software, open source scientific software, prediction, R, Rauch-Tung-Striebel, state-space models, statistics, stochastic algorithms, SVD, time series | 1 Comment

“Cauchy Distribution: Evil or Angel?” (from Xian)

Cauchy Distribution: Evil or Angel?. From Professor Christian Robert.

Posted in arXiv, Bayes, Bayesian, Cauchy distribution, information theoretic statistics, mathematics, maths, optimization, probabilistic programming, probability, rationality, reasonableness, statistics, stochastic algorithms, stochastics, Student t distribution | Leave a comment

“… the most patronizing start to an answer I have ever received …”

Professor Christian Robert tries to help out a student of MCMC on Cross Validated and earns the comment that his help had “the most patronizing start to an answer I have ever received“. I learned a new term: primitivus petitor.

Posted in Bayes, Bayesian, mathematics, mathematics education, maths, MCMC, optimization, reasonableness, statistics, stochastic algorithms | Leave a comment

“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…

Posted in Bayes, Bayesian, Gibbs Sampling, JAGS, MCMC, optimization, probabilistic programming, statistics, stochastic algorithms, stochastic search | Leave a comment