Category Archives: statistical ecology

What happens when time sampling density of a series matches its growth

This is the newly updated map of COVID-19 cases in the United States, updated, presumably, because of the new emphasis upon testing: How do we know this is the recent of recent testing? Look at the map of active cases: … Continue reading

Posted in American Association for the Advancement of Science, American Statistical Association, anti-intellectualism, anti-science, climate denial, corruption, data science, data visualization, Donald Trump, dump Trump, epidemiology, experimental science, exponential growth, forecasting, Kalman filter, model-free forecasting, nonlinear systems, open data, penalized spline regression, population dynamics, sampling algorithms, statistical ecology, statistical models, statistical regression, statistical series, statistics, sustainability, the right to know, the stack of lies | 1 Comment

“Code for causal inference: Interested in astronomical applications”

via Code for causal inference: Interested in astronomical applications From Professor Ewan Cameron at his Another Astrostatistics Blog.

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Reanalysis of business visits from deployments of a mobile phone app

This reports a reanalysis of data from the deployment of a mobile phone app, as reported in: M. Yauck, L.-P. Rivest, G. Rothman, “Capture-recapture methods for data on the activation of applications on mobile phones“, Journal of the American Statistical … Continue reading

Posted in Bayesian computational methods, biology, capture-mark-recapture, capture-recapture, Christian Robert, count data regression, cumulants, diffusion, diffusion processes, Ecological Society of America, ecology, epidemiology, experimental science, field research, Gibbs Sampling, Internet measurement, Jean-Michel Marin, linear regression, mark-recapture, mathematics, maximum likelihood, Monte Carlo Statistical Methods, multilist methods, multivariate statistics, non-mechanistic modeling, non-parametric statistics, numerics, open source scientific software, Pierre-Simon Laplace, population biology, population dynamics, quantitative biology, quantitative ecology, R, R statistical programming language, sampling, sampling algorithms, segmented package in R, statistical ecology, statistical models, statistical regression, statistical series, statistics, stepwise approximation, stochastic algorithms, surveys, V. M. R. Muggeo | Leave a comment

“Microplastics in the Ocean: Emergency or Exaggeration?” (Morss Colloquium, WHOI)

Update, 2019-10-28 00:34 ET I have compiled notes from the talks above, and from the audience Q&A and documented these in a Google Jam here.

Posted in American Association for the Advancement of Science, bag bans, Claire Galkowski, coastal communities, coasts, diffusion processes, microbiomes, microplastics, NOAA, oceanic eddies, oceanography, oceans, perceptions, phytoplankton, plastics, pollution, quantitative biology, quantitative ecology, science, science education, statistical ecology, WHOI, Woods Hole Oceanographic Institution | Leave a comment

cdetools package for R: Dalmasso, et al [updated]

Just hit the “arXiv streets”: N. Dalmasso, T. Pospisil, A. B. Lee, R. Izbicki, P. E. Freeman, A. I. Malz, “Conditional Density Estimation Tools in Python and R with applications to photometric redshifts and likelihood-free cosmological inference”, > astro-ph … Continue reading

Posted in ABC, accept-reject methods, astronomy, astrophysics, astrostatistics, Bayes, Bayesian computational methods, likelihood-free, statistical ecology | Leave a comment