sample_ctr

This is an alias for sample_conv_rate with 2 different input
columns. This function calculates posterior samples of
\(CTR = clicks/impressions\). Adds 2 new nested columns to
the input_df: `beta_params` and `samples`.
`beta_params` in each row should be a tibble of length 2 (\(\alpha\)
and \(\beta\) parameters)
`samples` in each row should be a tibble of length `n_samples`

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

sample_ctr function

<dl><dt>input_df</dt>
<dd>Dataframe containing option_name (str),
sum_clicks (dbl), and sum_impressions (dbl).</dd>
<dt>priors</dt>
<dd>Optional list of priors alpha0 and beta0.
Default \(Beta(1,1)\) will be use otherwise.</dd>
<dt>n_samples</dt>
<dd>Optional integer value. Defaults to 50,000 samples.</dd></dl>

Arguments

Sample Click Through Rate — sample_ctr

<dl>

<dt>input_df</dt>
<dd>Dataframe containing option_name (str),
sum_clicks (dbl), and sum_impressions (dbl).</dd>


<dt>priors</dt>
<dd>Optional list of priors alpha0 and beta0.
Default \(Beta(1,1)\) will be use otherwise.</dd>


<dt>n_samples</dt>
<dd>Optional integer value. Defaults to 50,000 samples.</dd>

</dl>

sample_ctr: Sample Click Through Rate

Description

Usage

Value

Arguments

Details