Hd-Deep-EM: Deep Expectation Maximization for Dynamic Hidden State Recovery Using Heterogeneous Data

Expectation-Maximization (EM) algorithm

The Expectation Maximization (EM) algorithm

In the previous class we already mentioned that many of the most powerful probabilistic models contain hidden variables. We will denote these variables with y. It is usually also the case that these models are most easily written in terms of their joint density, p(d,y,θ) = p(d|y,θ) p(y|θ) p(θ) (1) Remember also that the objective function we want to maximize is the log-likelihood (possibly incl...

Expectation-Maximization for Inverse Reinforcement Learning with Hidden Data

We consider the problem of performing inverse reinforcement learning when the trajectory of the agent being observed is partially occluded from view. Motivated by robotic scenarios in which limited sensor data is available to a learner, we treat the missing information as hidden variables and present an algorithm based on expectationmaximization to solve the non-linear, non-convex problem. Prev...

Dynamic Data Rectification Using the Expectation Maximization Algorithm

Although on-line measurements play a ®ital role in process control and monitoring ( process performance, they are corrupted by noise and occasional outliers such as noise ) spikes . Thus, there is a need to rectify the data by remo®ing outliers and reducing noise effects. Well-known techniques such as Kalman Filtering ha®e been used effecti®ely to filter noise measurements, but it is not design...

Statistical analysis of neural data: The expectation-maximization (EM) algorithm