Category Archives: numerical software

Cathy O’Neil’s WEAPONS OF MATH DESTRUCTION: A Review

(Revised and updated Monday, 24th October 2016.) Weapons of Math Destruction, Cathy O’Neil, published by Crown Random House, 2016. This is a thoughtful and very approachable introduction and review to the societal and personal consequences of data mining, data science, … Continue reading

Posted in citizen data, citizen science, citizenship, civilization, compassion, complex systems, criminal justice, Daniel Kahneman, data science, deep recurrent neural networks, destructive economic development, economics, education, engineering, ethics, Google, ignorance, Joseph Schumpeter, life purpose, machine learning, Mathbabe, mathematics, mathematics education, maths, model comparison, model-free forecasting, numerical analysis, numerical software, open data, optimization, organizational failures, planning, politics, prediction, prediction markets, privacy, rationality, reason, reasonableness, risk, silly tech devices, smart data, sociology, Techno Utopias, testing, the value of financial assets, transparency | Leave a comment

Repaired R code for Markov spatial simulation of hurricane tracks from historical trajectories

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 walk in a parameter space. … 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 | Leave a comment

“Holy crap – an actual book!”

Originally posted on mathbabe:
Yo, everyone! The final version of my book now exists, and I have exactly one copy! Here’s my editor, Amanda Cook, holding it yesterday when we met for beers: Here’s my son holding it: He’s offered…

Posted in American Association for the Advancement of Science, Buckminster Fuller, business, citizen science, citizenship, civilization, complex systems, confirmation bias, data science, data streams, deep recurrent neural networks, denial, economics, education, engineering, ethics, evidence, Internet, investing, life purpose, machine learning, mathematical publishing, mathematics, mathematics education, maths, moral leadership, multivariate statistics, numerical software, numerics, obfuscating data, organizational failures, politics, population biology, prediction, prediction markets, privacy, quantitative biology, quantitative ecology, rationality, reason, reasonableness, rhetoric, risk, Schnabel census, smart data, sociology, statistical dependence, statistics, the right to be and act stupid, the right to know, the value of financial assets, transparency, UU Humanists | Leave a comment

Bayesian blocks via PELT in R

The Bayesian blocks algorithm of Scargle, Jackson, Norris, and Chiang has an enthusiastic user community in astrostatistics, in data mining, and among some in machine learning. It is a dynamic programming algorithm (see VanderPlas referenced below) and, so, exhibits optimality … Continue reading

Posted in American Statistical Association, AMETSOC, anomaly detection, astrophysics, Cauchy distribution, changepoint detection, engineering, geophysics, multivariate statistics, numerical analysis, numerical software, numerics, oceanography, population biology, population dynamics, Python 3, quantitative biology, quantitative ecology, R, Scargle, spatial statistics, square wave approximation, statistics, stepwise approximation, time series, Woods Hole Oceanographic Institution | Leave a comment

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

data.table

R provides a helpful data structure called the “data frame” that gives the user an intuitive way to organize, view, and access data.  Many of the functions that you would us… Source: Intro to The data.table Package

Posted in big data, data science, engineering, numerical analysis, numerical software, numerics, open source scientific software, R, smart data, statistics | Leave a comment

On Smart Data

One of the things I find surprising, if not astonishing, is that in the rush to embrace Big Data, a lot of learning and statistical technique has been left apparently discarded along the way. I’m hardly the first to point … Continue reading

Posted in Akaike Information Criterion, Bayes, Bayesian, Bayesian inversion, big data, bigmemory package for R, changepoint detection, data science, data streams, dlm package, dynamic generalized linear models, dynamic linear models, dynamical systems, Generalize Additive Models, generalized linear models, information theoretic statistics, Kalman filter, linear algebra, logistic regression, machine learning, Markov Chain Monte Carlo, mathematics, mathematics education, maths, maximum likelihood, MCMC, Monte Carlo Statistical Methods, multivariate statistics, numerical analysis, numerical software, numerics, quantitative biology, quantitative ecology, rationality, reasonableness, sampling, smart data, state-space models, statistical dependence, statistics, the right to know, time series | 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

Phytoplankton-delineated oceanic eddies near Antarctica

Excerpt, from NASA: Phytoplankton are the grass of the sea. They are floating, drifting, plant-like organisms that harness the energy of the Sun, mix it with carbon dioxide that they take from the atmosphere, and turn it into carbohydrates and … Continue reading

Posted in AMETSOC, Antarctica, Arctic, bacteria, Carbon Cycle, complex systems, differential equations, diffusion, diffusion processes, dynamic linear models, dynamical systems, Emily Shuckburgh, environment, fluid dynamics, geophysics, GLMs, John Marshall, marine biology, Mathematics and Climate Research Network, NASA, numerical analysis, numerical software, oceanic eddies, oceanography, physics, phytoplankton, science, thermohaline circulation, WHOI, Woods Hole Oceanographic Institution | 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

November Hottest Ever, and Christmas Likely To bring Record Warmth in The East (Dan’s Wild Wild Science Journal; AGU Blogosphere)

The long-range guidance is showing strong indications that the incredible December warmth in the Eastern U.S. will continue to the end of the month. A blast of cold air will arrive later this week,… (Click on image for larger map, … Continue reading

Posted in AMETSOC, capricious gods, climate, climate models, Dan Satterfield, differential equations, diffusion processes, dynamical systems, ensembles, ENSO, environment, forecasting, geophysics, Hyper Anthropocene, meteorology, NCAR, NOAA, numerical software, physics, science, the right to know, wave equations | Leave a comment

“1D Wave with Delta Potential and Triangle Initial Position” (Jeff Galkowski, Stanford)

The latest calculations from Jeff Galkowski, of Stanford.

Posted in computation, engineering, Jeff Galkowski, mathematics, maths, McGill University, numerical analysis, numerical software, physics, proud dad, quantum, scattering, science, Stanford University, the right to know, University of California Berkeley, University of Rochester, wave equations, waves | Leave a comment

Southern Oscillation (SOI) correlated with Outgoing Longwave Radiation (OLR)

To the climate community this is nothing at all new, but I spotted these time series today and thought they would make a nice exhibit on how something people have direct control over, greenhouse gas emissions, affect a “teleconnection mechanism” … Continue reading

Posted in AMETSOC, bifurcations, carbon dioxide, climate, climate change, climate disruption, climate models, Dan Satterfield, differential equations, dynamic linear models, dynamical systems, ENSO, environment, forecasting, generalized linear models, geophysics, global warming, greenhouse gases, IPCC, Mathematica, mathematics, maths, meteorology, NCAR, NOAA, numerical software, oceanography, open data, physics, population biology, Principles of Planetary Climate, rationality, reasonableness, science, Spaceship Earth, state-space models, thermodynamics, time series | Leave a comment

Solar array with cloud predicting technology launched in WA

Australia’s first grid-connected solar power project with cloud predicting technology launched at Karratha Airport, WA, in bid to smooth solar supply. Source: Solar array with cloud predicting technology launched in WA

Posted in adaptation, Anthropocene, carbon dioxide, citizenship, civilization, clean disruption, climate, climate change, climate disruption, conservation, consumption, decentralized electric power generation, decentralized energy, demand-side solutions, dynamic linear models, efficiency, energy, energy reduction, energy utilities, engineering, environment, ethics, forecasting, geophysics, global warming, Hyper Anthropocene, investment in wind and solar energy, Kalman filter, mathematics, maths, meteorology, microgrids, mitigation, NCAR, numerical software, optimization, physics, prediction, probabilistic programming, rationality, reasonableness, risk, science, solar power, stochastics, sustainability, time series | 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

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 | Leave a comment