🧩 What This Introduces:

A Bayesian model for detecting changepoints in time series of overdispersed counts (for example, candidate contributions over a campaign or counts of terrorist violence). The model is built to identify salient structural breaks and to support formal inference on how many breaks appear in a given series.

🛠️ How Changepoints Are Identified:

The approach avoids specifying the number of changepoints ex ante by incorporating a hierarchical Dirichlet process prior. This lets the model:

• Estimate the number of changepoints from the data
• Estimate the location of each changepoint
• Accommodate overdispersed count observations without forcing simplistic variance assumptions

📊 Real-World Demonstrations:

Applications illustrate the model’s practical value:

• Campaign contributions during the 2012 U.S. Republican presidential primary
• Incidences of global terrorism from 1970 to 2015

These cases show how the method discovers structural breaks and supports inference about both the presence and number of changepoints.

🔎 Why It Matters:

Provides researchers and analysts a flexible, data-driven tool for detecting and quantifying regime shifts in count-based time series where variance exceeds simple Poisson assumptions. Especially relevant for empirical work on campaign dynamics, political violence, and other phenomena expressed as overdispersed counts.