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This technical note studies the identification of unknown decoherence rates for a class of two-level quantum systems undergoing spontaneous emission. Our previous work shows that estimates of the unknown decoherence rates can be obtained from the ensemble averages by imposing constant control and monitoring a sequence of identical systems continuously. Inspired by the work, this technical note further...
Based on the sensitivity-based approach, we discuss the reinforcement learning problem of semi-Markov decision processes (SMDPs) with average reward. First, we provide a new Bellman optimality equation. On this basis, we propose a relative value iteration (RVI) reinforcement learning algorithm. The new RVI reinforcement learning algorithm may avoid the estimation of optimal average reward in the process...
A fast learning algorithm for Hidden Markov Models is derived starting from convex divergence optimization. This method utilizes the alpha-logarithm as a surrogate function for the traditional logarithm to process the likelihood ratio. This enables the utilization of a stronger curvature than the logarithm. This paper's method includes the ordinary Baum-Welch re-estimation algorithm as a proper subset...
This paper derives the rate of convergence for the distribution free learning problem when the observation process is an exponentially strongly mixing (α-mixing with an exponential rate) Markov chain. If {zk}K=1∞ = {(xk, yk)}k=1∞ ⊂ x × Y ≡ Z is an exponentially strongly mixing Markov chain with stationary measure ρ, it is shown that the empirical estimate fz that minimizes the discrete quadratic risk...
Aiming at large initial attitude errors of flight object, this paper presents perfect coupling sampling based on coupling from the past (CFTP) algorithm on MCMC (Markov chain Monte Carlo) to tackle the problem of sequential flight object attitude estimation. Based Bayesian theory, posterior distribution can be approximated by Monte Carlo likelihood function and conjunction prior distribution based...
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