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We present an online method for joint state and parameter estimation in jump Markov non-linear systems (JMNLS). State inference is enabled via the use of particle filters which makes the method applicable to a wide range of non-linear models. To exploit the inherent structure of JMNLS, we design a Rao-Blackwellized particle filter (RBPF) where the discrete mode is marginalized out analytically. This...
We examine the problem of filtering for dynamic probabilistic systems using Markov Logic Networks. We propose a method to approximately compute the marginal probabilities for the current state variables that is suitable for online inference. Contrary to existing algorithms, our approach does not work on the level of belief propagation, but can be used with every algorithm suitable for inference in...
A novel infinite-horizon policy-gradient estimation method with variable discount factor is proposed in this paper. This method tackles the normal policy-gradient estimation methods' limitations on unbalance of the bias and variance by using an incremental sequence as the discount factor. Numerical experiments conducted on the Markov decision process have shown its effectiveness.
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