This document summarizes a talk given by Heiko Strathmann on using partial posterior paths to estimate expectations from large datasets without full posterior simulation. The key ideas are: 1. Construct a path of "partial posteriors" by sequentially adding mini-batches of data and computing expectations over these posteriors. 2. "Debias" the path of expectations to obtain an unbiased estimator of the true posterior expectation using a technique from stochastic optimization literature. 3. This approach allows estimating posterior expectations with sub-linear computational cost in the number of data points, without requiring full posterior simulation or imposing restrictions on the likelihood. Experiments on synthetic and real-world examples demonstrate competitive performance versus standard M