update_beta

Updates Beta Distribution with the Beta-Bernoulli
conjugate prior update rule

Uses simple Bayesian conjugate prior update rules to calculate
the win probability of each option, value remaining in the test, and
percent lift over the baseline for various marketing objectives.
References:
Fink, Daniel (1997) "A Compendium of Conjugate Priors" <https://www.johndcook.com/CompendiumOfConjugatePriors.pdf>.
Stucchio, Chris (2015) "Bayesian A/B Testing at VWO" <https://vwo.com/downloads/VWO_SmartStats_technical_whitepaper.pdf>.

Ryan Angi

grizbayr

Bayesian Inference for A|B and Bandit Marketing Tests

update_beta function

<dl><dt>alpha</dt>
<dd>Double value for alpha (count of successes). Must be 0 or greater.</dd>
<dt>beta</dt>
<dd>Double value for beta (count of failures). Must be 0 or greater.</dd>
<dt>priors</dt>
<dd>An optional list object that contains alpha0 and
beta0. Otherwise the function with use Beta(1,1) as the prior distribution.</dd></dl>

Arguments

Update Beta — update_beta

<dl>

<dt>alpha</dt>
<dd>Double value for alpha (count of successes). Must be 0 or greater.</dd>


<dt>beta</dt>
<dd>Double value for beta (count of failures). Must be 0 or greater.</dd>


<dt>priors</dt>
<dd>An optional list object that contains alpha0 and
beta0. Otherwise the function with use Beta(1,1) as the prior distribution.</dd>

</dl>

update_beta: Update Beta

Description

Usage

Value

Arguments

Examples