Category Archives: Python 3

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

R and “big data”

On 2nd November 2015, Wes McKinney, the developer of the highly useful Python pandas module (and other things, including books), wrote an amusing blog post, “The problem with the data science language wars“. I by no means disagree with him. … Continue reading

Posted in Bayes, Bayesian, big data, bigmemory package for R, Jay Emerson, MCMC, numerics, Python 3, R, Yale University Statistics Department | Leave a comment

The CWSLab workflow tool: an experiment in community code development

Originally posted on Dr Climate:
Give anyone working in the climate sciences half a chance and they’ll chew your ear off about CMIP5. It’s the largest climate modelling project ever conducted and formed the basis for much of the IPCC…

Posted in climate, climate education, climate models, computation, differential equations, dynamical systems, environment, forecasting, geophysics, global warming, IPCC, mathematics, mathematics education, maths, meteorology, model comparison, NCAR, oceanography, open source scientific software, physics, Principles of Planetary Climate, Python 3, rationality, reasonableness, science, science education, state-space models, statistics, time series, transparency | Leave a comment

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 | 4 Comments

Dynamic Linear Models package, dlmodeler

I’m checking out the dlmodeler package in R for a work project. It is accompanied by textbooks, G. Petris, S. Petrone, P. Campagnoli, Dynamic Linear Models with R, Springer, 2009 and J. Durbin, S. J. Koopman, Time Series Analysis by … Continue reading

Posted in Bayesian, geophysics, mathematics, maths, oceanography, open source scientific software, Python 3, R, science, sea level rise, state-space models, statistics, stochastic algorithms, time series | Leave a comment

R vs Python: Practical Data Analysis

R vs Python: Practical Data Analysis (Nonlinear Regression).

Posted in Bayes, Bayesian, biology, climate change, ecology, environment, Python 3, R, statistics, Wordpress | Leave a comment