From the waves to the redwoods, it's all happening here.

Join us Wednesday, May 21st from 12:00 PM- 1:00 PM at California Coffee for an informative and uplifting workshop you won't want tfor suo miss! Our featured guest speaker, Manas S. Kshirsagar, AD, MS a NAMA Board Certified Ayurvedic Doctor with years of experience in holistic wellness and healing, will be presenting "Tools for Stress and Anxiety". With today's fast-paced world and the stress levels many of us are unduring, this workshop will offer valuable insight, practical tools and wellness strategies to help bring more balance, peace and calm into our daily lives. Lunch Special Available: Half Sandwich + Chips just $9.99 California Coffee, 9105 Soquel Dr., Aptos, CA 95003 cacoffeeaptos.com Thank you from myself Sylvia Reyes, and my amazing team for supporting our small business and community events!

Many scientific applications produce repeated point pattern realizations across subjects, regions, or time. While such point patterns exhibit individual variation, we assume they arise from related point processes that share a common distributional structure. This dissertation develops a Bayesian nonparametric modeling framework built around an interpretable baseline. We work with Poisson processes, such that the point process stochastic mechanism is characterized by the total intensity and a density with compact support. Flexible, parsimonious weighted combinations of beta densities represent both the baseline and the replicate-specific densities. The weights corresponding to each replicate encode the features that characterize its density and are pooled hierarchically across replicates to estimate the shared baseline. Throughout, we illustrate the framework using bike-share demand at a Chicago Divvy station, where weekly demand shares a common daily pattern while its features evolve across weeks. We lay the foundation with a conditionally independent model, relating the replicates through a Dirichlet process prior centered on the shared baseline. The model admits a Pólya urn representation that yields fully conjugate posterior updates and partially parallel computation across replicates. Inference proceeds at both the shared and replicate-specific levels, and predictive inference on the density of demand for a new week follows readily through the baseline. Moving to a dynamic extension, we use the representation of the replicate-specific discrete random distributions to express structured dependence separately through the atoms and the weights. Stochastic processes on these components relate the locations of density features and their relative importance. The weights follow a geometric construction that keeps the model parsimonious. For the atoms, we develop a novel stochastic process with the baseline as its marginal distribution, so its interpretation is unchang