Bayesian inference via rejection filtering

Abstract

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.

References in corpus (4)

• Expectation Propagation for approximate Bayesian inference
• Suppressing qubit dephasing using real-time Hamiltonian estimation
• Efficient Bayesian Phase Estimation
• Quantum Inspired Training for Boltzmann Machines

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• Practical Quantum Computing: The value of local computation