Posterior statistics

The methods listed below are defined in src/stats.jl.

StatsBase.autocor — Function

autocor(chains[; lags = [1, 5, 10, 50], demean = true, append_chains = true, kwargs...])

Compute the autocorrelation of each parameter for the chain.

Setting append_chains=false will return a vector of dataframes containing the autocorrelations for each chain.

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DataAPI.describe — Function

describe(io, chains[;
         q = [0.025, 0.25, 0.5, 0.75, 0.975],
         etype = :bm,
         kwargs...])

Print the summary statistics and quantiles for the chain.

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Statistics.mean — Function

mean(chains[, params; kwargs...])

Calculate the mean of a chain.

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StatsBase.summarystats — Function

function summarystats(
    chains;
    sections = _default_sections(chains),
    append_chains= true,
    method::AbstractESSMethod = ESSMethod(),
    maxlag = 250,
    etype = :bm,
    kwargs...
)

Compute the mean, standard deviation, naive standard error, Monte Carlo standard error, and effective sample size for each parameter in the chain.

Setting append_chains=false will return a vector of dataframes containing the summary statistics for each chain.

When estimating the effective sample size, autocorrelations are computed for at most maxlag lags.

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Statistics.quantile — Function

quantile(chains[; q = [0.025, 0.25, 0.5, 0.75, 0.975], append_chains = true, kwargs...])

Compute the quantiles for each parameter in the chain.

Setting append_chains=false will return a vector of dataframes containing the quantiles for each chain.

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