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   State Space Models: A Modern Approach
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   Scratchpad
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     Hidden Markov Models
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     Hidden Semi-Markov Models
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     Linear Gaussian SSMs
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     Non-Linear Gaussian SSMs
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     Non-Gaussian SSMs
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     Switching SSMs
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     Deep SSMs
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     Recurrent Neural Networks
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   Inference in discrete SSMs
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     HMM filtering (forwards algorithm)
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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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   Inference in linear-Gaussian SSMs
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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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     Unscented 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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     Expectation Maximization (EM)
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     Stochastic Gradient Descent (SGD)
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     Variational Bayes (VB)
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     Markov Chain Monte Carlo (MCMC)
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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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<h1>ssm-book<a class="headerlink" href="#ssm-book" title="Permalink to this headline">¶</a></h1>
<p>Interactive textbook on state-space models.
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