Longitudinal data analysis is an essential statistical approach for studying phenomena observed repeatedly over time, allowing researchers to explore both within-subject and between-subject variations ...

Bayesian analysis offers a robust framework for deciphering the intricate dynamics of time series data. By treating unknown parameters as random variables, this approach incorporates prior information ...

As rare disease trials face persistent feasibility challenges, Bayesian designs are gaining momentum by enabling more ...

The FDA has released draft guidance on how sponsors can use Bayesian models for clinical trials.

Approaches for statistical inference -- The Bayes approach -- Bayesian computation -- Model criticism and selection -- The empirical Bayes approach -- Bayesian design -- Special methods and models -- ...

Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...

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 ...

In this video interview, David Morton, PhD, director of biostatistics at Certara, explains how regulatory momentum is ...

This course offers a rigorous yet practical exploration of Bayesian reasoning for data-driven inference and decision-making. Students will gain a deep understanding of probabilistic modeling, and ...

This course is available on the MPA in Data Science for Public Policy, MSc in Data Science, MSc in Health Data Science, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics ...