aha123
Member
- Joined
- May 25, 2011
- Member Type
- Student or Learner
- Native Language
- Japanese
- Home Country
- Japan
- Current Location
- United States
English's articles are damned difficult to get it right!
Here is my writing. I am not sure if I am correct for articles use around the words highlighted with red. Correction for other errors is welcomed.
Bayesian approach is a different statistical school of inference than that of frequentist approach. Bayesian approach starts with a prior belief about a parameter of interest and then updates this belief as more evidence/data is collected. Such approach is well suited for sequential test settings.
Here we focus on how to use Bayesian approach for conversion rate A/B tests. It can be easily extended to other metrics. Data from conversion rate tests can be modeled as a binomial sequence with parameter p can be expressed as Beta(a, b). After N samples with S successes are collected from the test, the updated belief about p can be expressed by a posterior probability distribution. Using Bayes theorem, it can be proved that this posterior probability distribution for p equals to Beta(S+a,N−S+b). Therefore, throughout the test, we only need to keep track of 2 numbers, N and S, to update the posterior probability distribution of p for a binomial data sequence.
Here is my writing. I am not sure if I am correct for articles use around the words highlighted with red. Correction for other errors is welcomed.
Bayesian approach is a different statistical school of inference than that of frequentist approach. Bayesian approach starts with a prior belief about a parameter of interest and then updates this belief as more evidence/data is collected. Such approach is well suited for sequential test settings.
Here we focus on how to use Bayesian approach for conversion rate A/B tests. It can be easily extended to other metrics. Data from conversion rate tests can be modeled as a binomial sequence with parameter p can be expressed as Beta(a, b). After N samples with S successes are collected from the test, the updated belief about p can be expressed by a posterior probability distribution. Using Bayes theorem, it can be proved that this posterior probability distribution for p equals to Beta(S+a,N−S+b). Therefore, throughout the test, we only need to keep track of 2 numbers, N and S, to update the posterior probability distribution of p for a binomial data sequence.