Distributed Solar: The Democratizaton of Energy
Blogroll
- Charlie Kufs' "Stats With Cats" blog
- In Monte Carlo We Trust
- Pat's blog
- Awkward Botany
- Giant vertical monopolies for energy have stopped making sense
- Risk and Well-Being
- Simon Wood's must-read paper on dynamic modeling of complex systems
- James' Empty Blog
- BioPython
- Bob Altemeyer on authoritarianism (via Dan Satterfield)
climate change
- Isaac Held's blog
- Wally Broecker on climate realism
- History of discovering Global Warming
- James Powell on sampling the climate consensus
- Agendaists
- Climate Change Reports
- “Ways to [try to] slow the Solar Century''
- Interview with Wally Broecker
- Mrooijer's Global Temperature Explorer
- Simple models of climate change
Archives
Jan Galkowski
Category Archives: Markov chain random fields
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
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ecoquant
