# Bumps 0.7.6 documentation

## stats - Statistics helper functions

`VarStats` |
`var_stats` |
`format_vars` |
`parse_var` | Parse a line returned by format_vars back into the statistics for the variable on that line.
`stats` | Find mean and standard deviation of a set of weighted samples.
`credible_intervals` | Find the credible interval covering the portion ci of the data.

Statistics helper functions.

class `bumps.dream.stats.``VarStats`(**kw)

  Bases: `object`

`bumps.dream.stats.``var_stats`(draw, vars=None)

`bumps.dream.stats.``format_vars`(all_vstats)

`bumps.dream.stats.``parse_var`(line)

  Parse a line returned by format_vars back into the statistics for the variable on that line.

`bumps.dream.stats.``stats`(x, weights=None)

  Find mean and standard deviation of a set of weighted samples.

  Note that the median is not strictly correct (we choose an endpoint of the sample for the case where the median falls between two values in the sample), but this is good enough when the sample size is large.

`bumps.dream.stats.``credible_intervals`(x, ci, weights=None)

  Find the credible interval covering the portion ci of the data.

  x are samples from the posterior distribution.

  ci is a set of intervals in [0,1]. For a \(1-\sigma\) interval use ci=erf(1/sqrt(2)), or 0.68. About 1e5 samples are needed for 2 digits of precision on a \(1-\sigma\) credible interval. For a 95% interval, about 1e6 samples are needed.

  weights is a vector of weights for each x, or None for unweighted. One could weight points according to temperature in a parallel tempering dataset.

  Returns an array [[x1_low, x1_high], [l2_low, x2_high], …] where [xi_low, xi_high] are the starting and ending values for credible interval i.

  This function is faster if the inputs are already sorted.