Daniel Saunders

Daniel Saunders, a philosopher-turned-Bayesian practitioner who builds decision-theory workflows and PyMC/PyTensor tooling. He discusses separating beliefs from utilities for team workflows. He explains shifting evaluation from accuracy to business value and demonstrates vectorized posterior optimization with PyTensor for pricing and profit. He covers risk-averse utility transforms and practical safeguards for industrial decision making.