Naive Bayesian Algorithm

Naive Bayesian Algorithm uses a statistical approach: the Bayesian approach is robust and less likely to find false patterns in noisy data (overfit the data).

Bayes theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

$$P(A|B) = \frac{P(B|A) P(A)}{P(B)}$$

where $P(A)$ is the probability of event A occuring, P(A|B) is the probability A conditioned on B ocuring. $A$ is the desired outcome.

Bayesian inference allows to make justified decisions on a granular level by modeling the variationin the observed data.

roas= revenue/cost = revenue / conversions conversions/cost= revenue/conversion 1/cpa

we can assume that the ad sets are similar to each other:

Log-normal probability density function:

$$y= f(x|\mu, \sigma) = \frac{1}{x \sigma \sqrt{2 \pi}} exp\left( -\frac{(ln x - \mu)}{}2 \sigma^2 \right)$$