In stefanocoretta/bayes.handson: Learning materials for the learnB4SS workshop

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Family of distributions : Distributions that can be described by the same set of parameters make up a family of distributions. For example the normal/Gaussian family includes all of the possible distributions that can be described by the two parameters $\mu$ and $\sigma$ alone. : In brms you set the family of the probability distribution of the outcome variable with the argument family. For example, family = gaussian(), family = bernoulli(). : In practice, you can think of the family as the prior probability distribution of the outcome variable.

Likelihood : This is the probability distribution of the outcome variable conditional on the prior(s). : Notation: $p(d|\theta)$.

Outcome/response/dependent variables : These are the variables that appear on the left-hand side of a model formula. For example, f0 in f0 ~ attitude; F1 and F2 in c(F1, F2) ~ stress.

Parameter : A parameter in a statistical model (for example intercept/mean, standard deviation, slope/$\beta$, etc). : A parameter used to describe a probability distribution (for example $\mu$ and $\sigma$ for normal/Gaussian distributions).

Predictors, independent variables : These are the variable that appear on the right-hand side of a model formula. For example, novel_word in reaction_t ~ novel_word; s(longitude, latitude) in temperature ~ s(longitude, latitude).

Prior probability distribution or simply prior : This is the probability distribution of the values a parameter can take, based on prior knowledge/belief, domain expertise, previous research, pilot data. : Notation: $p(\theta)$.

stefanocoretta/bayes.handson documentation built on Oct. 3, 2023, 11:03 p.m.