radu
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SSMBook
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   State Space Models: A Modern Approach
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   State Space Models
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     What are State Space Models?
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     Hidden Markov Models
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     Linear Gaussian SSMs
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     Nonlinear Gaussian SSMs
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     States estimation (inference)
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     Parameter estimation (learning)
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     HMM smoothing (forwards-backwards algorithm)
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     Viterbi algorithm
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     Parallel HMM  smoothing
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     Forwards-filtering backwards-sampling algorithm
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     Kalman filtering
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     Kalman (RTS) smoother
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     Parallel Kalman Smoother
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     Forwards-filtering backwards sampling
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   Extended (linearized) methods
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     Extended Kalman filtering
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     Extended Kalman smoother
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     Parallel extended Kalman smoothing
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   Quadrature and cubature methods
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   Posterior linearization
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   Assumed Density Filtering
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   Variational inference
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   Particle filtering
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   Sequential Monte Carlo
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   Offline parameter estimation (learning)
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   Multi-target tracking
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   Data assimilation using Ensemble Kalman filter
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   Bayesian non-parametric SSMs
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   Changepoint detection
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   Timeseries forecasting
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   Markovian Gaussian processes
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   Differential equations and SSMs
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   Optimal control
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