Researchers developed a new method to analyze changes in data over time using Bayesian statistics. They used a technique called the Gibbs sampler to estimate the probability distributions of different scenarios without complex calculations. This method was applied to various problems like changing regression patterns, Poisson processes, and Markov chains, solving previously difficult issues in these areas.