This paper gives the details of a useful algorithm that speeds up the simulation of state vectors from a state space model. The algorithm runs very quick as compared to other methods. I ran the algorithm for a simple local level model inference via Gibbs sampling and found the speed to be considerably faster than other Forward Filter Backward Sampling algorithms. For a more generic Bayesian inference, this algorithm will no doubt cut the computation time significantly.

A lovely article written by Shiv Visvanathan : My father loved to walk. It was his great ritual, his idea of prayer and work. Every morning at four, the house would echo with the thump of his shoes, the tumbler of coffee, as he hurried out. My dachshund, a wise ten-year-old would wait impatiently, grumbling melodramatically about any delay. Whoever talked of walking a dog never understood man or beast.

When the going gets tough, the tough lower their standards! - Dr. Sanjoy Mahajan (Street Fighting Math)

Classification trees fail miserably in some cases and in such situations, bumping might be a good method. A stylized example of bumping is as follows : Imagine that there are two covariates x1 and x2 and the true class labels dependend on XORing the two covariates. The orange labels represent one class and blue labels represent another class. If you run any sort of plain vanilla classification algorithm that does greedy binary splits, the algo will fail.

Link : Detailed Summary of the book Takeaway: dlm package in R is one of the best resources out there in the open source community that can be used for DLM inference. The fact that one of the authors is also the contributors to the package has made this book apt for practitioners. However the book is best understood after having a working knowledge of Bayesian inference. By understanding and thinking in State space framework, a modeler gets many more options to model univariate or multivariate time series data.