Category Archives: dynamic generalized linear models

Results of short literature search on impacts of climate change upon ecosystems and bird or animal migration patterns, from the journals of the Ecological Society of America

I decided to do a quick literature search on the impacts of climate change upon ecosystems and migration patterns. I could have kept the list private, but why not make it public? Not all these articles are purely about the … Continue reading

Posted in adaptation, American Statistical Association, Anthropocene, biology, climate change, climate education, climate models, complex systems, differential equations, dynamic generalized linear models, dynamical systems, ecological services, Ecological Society of America, ecology, Ecology Action, environment, evidence, global warming, Hyper Anthropocene, marine biology, mass extinctions, nonlinear systems, population biology, population dynamics, quantitative biology, quantitative ecology, tragedy of the horizon | Leave a comment

Chesterton’s fence, ecological sensitivity, and the disruption of ecological services

Hat tip to Matt Levine for introducing me to the term Chesteron’s fence: Chesterton’s fence is the principle that reforms should not be made until the reasoning behind the existing state of affairs is understood. … In the matter of … Continue reading

Posted in dynamic generalized linear models, dynamical systems, ecological services, ecology, Ecology Action, mathematics, mathematics education, maths, XKCD | 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