Machine

Bayesian hierarchical models have become increasingly popular in statistical modeling and data analysis, especially when dealing with complex datasets that involve multiple levels of variability. These models provide a structured way to incorporate both group-level and individual-level information, allowing analysts to make more precise inferences. Python, with its rich ecosystem of libraries for probabilistic programming … Read more