Accelerating Monte Carlo Bayesian Inference via Approximating Predictive Uncertainty over Simplex

Understand

Estimating the predictive uncertainty of a Bayesian learning model is critical in various decision-making problems, e.g., reinforcement learning, detecting adversarial attack, self-driving car.

• As the model posterior is almost always intractable, most efforts were made on finding an accurate approximation the true posterior.
• Even though a decent estimation of the model posterior is obtained, another approximation is required to compute the predictive distribution over the desired output.
• A common accurate solution is to use Monte Carlo (MC) integration.