Bayesian inference via rejection filtering
This work addresses computational bottlenecks in Bayesian inference for applications requiring online parameter tracking and classification, though it appears incremental as it builds on existing rejection sampling and particle filtering techniques.
The paper tackles the problem of approximating Bayesian inference by introducing rejection filtering, which combines rejection sampling with particle filtering to improve efficiency and reduce memory usage compared to conventional methods. The results demonstrate its ability to track time-dependent parameters in online settings and achieve competitive performance on MNIST classification benchmarks.
We provide a method for approximating Bayesian inference using rejection sampling. We not only make the process efficient, but also dramatically reduce the memory required relative to conventional methods by combining rejection sampling with particle filtering. We also provide an approximate form of rejection sampling that makes rejection filtering tractable in cases where exact rejection sampling is not efficient. Finally, we present several numerical examples of rejection filtering that show its ability to track time dependent parameters in online settings and also benchmark its performance on MNIST classification problems.