Category Archives: empirical likelihood

Stream flow and P-splines: Using built-in estimates for smoothing

Posted on 28 January 2019 by

Mother Brook in Dedham Massachusetts was the first man-made canal in the United States. Dug in 1639, it connects the Charles River at Dedham, to the Neponset River in the Hyde Park section of Boston. It was originally an important … Continue reading →

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Posted in American Statistical Association, citizen data, citizen science, Clausius-Clapeyron equation, Commonwealth of Massachusetts, cross-validation, data science, dependent data, descriptive statistics, dynamic linear models, empirical likelihood, environment, flooding, floods, Grant Foster, hydrology, likelihood-free, meteorological models, model-free forecasting, non-mechanistic modeling, non-parametric, non-parametric model, non-parametric statistics, numerical algorithms, precipitation, quantitative ecology, statistical dependence, statistical series, stream flow, Tamino, the bootstrap, time series, water vapor | 2 Comments

Sampling: Rejection, Reservoir, and Slice

Posted on 29 September 2018 by

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 →

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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

“Causal feedbacks in climate change”

Posted on 20 January 2016 by

Today I was reviewing and re-reading the nonlinear time series technical literature I have, seeking ideas on how to go about using the statistical ensemble learning technique called “boosting” with them. (See the very nice book, R. E. Schapire, Y. … Continue reading →

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Posted in Anthropocene, boosting, Carbon Cycle, carbon dioxide, Carbon Worshipers, cat1, climate, climate change, climate data, climate disruption, complex systems, convergent cross-mapping, denial, differential equations, diffusion processes, dynamical systems, ecology, Egbert van Nes, empirical likelihood, ensembles, environment, Ethan Deyle, Floris Takens, forecasting, fossil fuels, geophysics, George Sughihara, global warming, greenhouse gases, Hao Ye, machine learning, Maren Scheffer, mathematics, maths, meteorology, physics, rationality, reasonableness, science, state-space models, Takens embedding theorem, time series, Timothy Lenton, Victor Brovkin | 2 Comments

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

Posted on 24 November 2015 by

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/.

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