Category Archives: Bayes

Naomi Oreskes and significance testing

Naomi Oreskes has an op-ed in The New York Times today, which intends to defend the severe standards of evidence scientists employ, with special applicability to climate science and their explanation of causation (greenhouse gases produce radiative forcing), attribution (most … Continue reading

Posted in Bayes, Bayesian, citizen science, climate, climate education, mathematics, mathematics education, maths, model comparison, rationality, reasonableness, science, statistics, testing | Leave a comment

On nested equivalence classes of climate models, ordered by computational complexity

I’m digging into the internals of ABC, for professional and scientific reasons. I’ve linked a great tutorial elsewhere, and argued that this framework, advanced by Wood, and Wilkinson (Robert), and Wilkinson (Darren), and Hartig and colleagues, and Robert and colleagues, … Continue reading

Posted in approximate Bayesian computation, Bayes, Bayesian, biology, ecology, environment, forecasting, geophysics, IPCC, mathematics, maths, MCMC, meteorology, NCAR, NOAA, oceanography, optimization, population biology, Principles of Planetary Climate, probabilistic programming, R, science, stochastic algorithms, stochastic search | Leave a comment

“[W]e want to model the process as we would simulate it.”

Professor Darren Wilkinson offers a pithy insight on how to go about constructing statistical models, notably hierarchical ones: “… we want to model the process as we would simulate it ….” This appears in his blog post One-way ANOVA with … Continue reading

Posted in approximate Bayesian computation, Bayes, Bayesian, biology, ecology, engineering, forecasting, mathematics, mathematics education, maths, model comparison, optimization, population biology, probabilistic programming, rationality, reasonableness, risk, science, science education, sociology, statistics, stochastic algorithms | Tagged | Leave a comment

struggling with problems already partly solved by others

Climate modelers and models see as their frontier the problem of dealing with spontaneous dynamics in systems such as atmosphere or ocean which are not directly forced by boundary conditions such as radiative forcing due to increased greenhouse gas (“GHG”) … Continue reading

Posted in approximate Bayesian computation, Bayes, Bayesian, biology, climate, climate education, differential equations, ecology, engineering, environment, geophysics, IPCC, mathematics, mathematics education, meteorology, model comparison, NCAR, NOAA, oceanography, physics, population biology, probabilistic programming, rationality, reasonableness, risk, science, science education, statistics, stochastic algorithms, stochastic search | 1 Comment

illustrating particle filters and Bayesian fusion using successive location estimates on the unit circle

Introduction Modern treatments of Bayesian integration to obtain posterior densities often use some form of Markov Chain Monte Carlo (“MCMC”), typically Gibbs sampling. Gibbs works well with many Bayesian hierarchical models. The standard problem-solving situation with these is that a … Continue reading

Posted in Bayes, Bayesian, biology, mathematics, maths, population biology, probabilistic programming, R, statistics, stochastic algorithms | 1 Comment

“… making a big assumption …”

“That’s making a big assumption.” (This post is a follow-on from an earlier one.) In the colloquial, the phrase means basing an argument on a precondition which is unusual or atypical or offends common sense. When applied to scientific hypotheses, … Continue reading

Posted in Bayes, Bayesian, climate, climate education, environment, geophysics, information theoretic statistics, mathematics, maths, meteorology, model comparison, oceanography, physics, rationality, reasonableness, risk, statistics | 1 Comment

Bayesian inference works even in a chaotic or deterministic world

Professor John Geweke, in a Comment on an article by Professor Mark Berliner a bit back (1992), shows how Bayesian inference continues to be a means for expressing subjective uncertainty even in a scheme where there are no stochastics but … Continue reading

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“Can we trust climate models?”

J. C. Hargreaves, J. D. Annan, “Can we trust climate models?”, WIREs Climate Change 2014, 5:435–440. doi: 10.1002/wcc.288. See also D. A. Stainforth, T. Aina, C. Christensen, M. Collins, N. Faull, D. J. Frame, J. A. Kettleborough, S. Knight, A. … Continue reading

Posted in Bayes, Bayesian, climate, climate education, differential equations, ecology, forecasting, geophysics, IPCC, mathematics education, meteorology, NCAR, NOAA, physics, rationality, reasonableness, risk, science, science education, statistics, stochastic algorithms | 1 Comment

probabilistic discussions of climate policy

Posted in Bayes, Bayesian, citizen science, citizenship, civilization, climate, climate education, ecology, economics, education, engineering, mathematics education, optimization, politics, rationality, reasonableness, risk, science education, statistics | Leave a comment

Liddell and Kruschke, on conditional logistic Bayesian estimation

(“Ostracism and fines in a public goods game with accidental contributions: The importance of punishment type”) An overview. The article

Posted in Bayes, Bayesian, biology, citizenship, civilization, compassion, ecology, economics, ethics, humanism, investing, MCMC, politics, rationality, reasonableness, risk, sociology, statistics | Leave a comment

An equation-free introduction to Bayesian inference

By Tomoharu Eguchi from 2008: “An Introduction to Bayesian Statistics Without Using Equations“.

Posted in Bayes, Bayesian, BUGS, JAGS, mathematics, mathematics education, maths, probabilistic programming, rationality, reasonableness, science education, statistics | Leave a comment