Reasons to Use Bayesian Inference (statmodeling.stat.columbia.edu)

A concise manifesto: the author lays out seven practical reasons to use Bayesian inference—decision analysis (plugging posterior uncertainty into expected-utility calculations), propagation of uncertainty (use posterior simulations to get uncertainty for any function of parameters or predictions, e.g., the x-intercept -a/b), incorporating prior information, regularization via informative priors, combining multiple data sources (multilevel models, MRP, soft constraints across trials), modeling latent data/parameters, and enabling the fitting of models that are too large or complex for traditional point-estimation methods. Each point emphasizes a different practical scenario where Bayesian machinery naturally solves common statistical challenges in modeling, inference, and prediction. For AI/ML practitioners the takeaways are concrete: posterior simulation and probabilistic modeling make it straightforward to carry uncertainty through downstream decisions and derived quantities; hierarchical and informative-prior structures let you pool information and stabilize estimates in sparse-data regimes; treating unobserved quantities as latent variables expands the types of generative models you can fit; and Bayesian workflows often let you push modeling frontiers rather than resorting to simplifying approximations. The author also notes trade-offs—Bayesian methods require effort and many benefits (regularization, latent modeling, uncertainty quantification) can be achieved with non-Bayesian tools—but argues that the Bayesian framework coherently packages these capabilities, making it a powerful option for complex AI/ML problems.