Supplement paper to "Online Expectation Maximization based algorithms for inference in hidden Markov models"
This is a supplementary material to the paper "Online Expectation Maximization based algorithms for inference in hidden Markov models". It contains further technical derivations and additional simulation results.
Paper
References (11)
09A new method of stochastic approximation type1990 · Autom Remote Control
11θ ∈ Θ and the rhs is integrable under the stated assumptions. Therefore, by the dominated convergence theorem, E [ ∇ θ δ θ ( Y )] = ∇ θ E [ δ θ ( Y )] = ∇ θ (cid:96) ( θ )the proof