Sequential Bayesian Inference for Dynamic State Space Model Parameters
نویسندگان
چکیده
Dynamic state-space models [24], consisting of a latent Markov process X0, X1, . . . and noisy observations Y1, Y2, . . . that are conditionally independent, are used in a wide variety of applications e.g. wireless networks [8], object tracking [21], econometrics [7] etc. The model is specified by an initial distribution p(x0|✓), a transition kernel p(xt|xt 1, ✓) and an observation distribution p(yt|xt, ✓). These distributions are defined in terms of a set of K static (e.g. non-time varying) parameters ✓ = (✓1, . . . , ✓K). The joint model to time T is:
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