Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

The past decade has witnessed significant advances in causal inference and Bayesian network learning, two intertwined disciplines that allow researchers to discern underlying cause‐and‐effect ...

In the ever-evolving toolkit of statistical analysis techniques, Bayesian statistics has emerged as a popular and powerful methodology for making decisions from data in the applied sciences. Bayesian ...

For likelihood-based inferences from data with missing values, models are generally needed for both the data and the missing-data mechanism. However, modeling the mechanism can be challenging, and ...

Here are 11 free NPTEL data science and analytics courses from leading IITs cover graph theory, Bayesian modelling, Python, R, databases and big-data stats. These are all free to audit, and enrolment ...

Whether in everyday life or in the lab, we often want to make inferences about hypotheses. Whether I’m deciding it’s safe to run a yellow light, when I need to leave home in order to make it to my ...