In an earlier post, we discussed how to derive the posterior distribution for the population mean. In this post, we will focus on deriving the posterior distribution for the variance parameter which is used in different types of Bayesian inference. Background Context In a lot of different Bayesian contexts (e.g., hierarchical Bayesian linear regression, hierarchical […]
Background Context In a lot of different Bayesian contexts (e.g., hierarchical Bayesian linear regression, hierarchical Bayesian estimation of discrete choice models etc), the following situation arises: There are \(n\) respondents whose response can be modeled by a set of independent variables and associated parameters. We will denote these parameters by \(\beta_i\) . We assume […]
Processing open ended consumer responses used to be time consuming. The usual process involved categorizing the response into several categories and then summarize the themes that emerge. Natural language processing (NLP) libraries can help with the task of analyzing open ended responses to assess consumer sentiment, aspects of the experience consumers are talking about the […]
Using confidence intervals is one approach to identify if the proposed version of an A/B test is achieving business objectives relative to baseline. This post discusses what confidence intervals are, how to interpret them and the impact of sample sizes on the confidence intervals.