The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modeled separately. These models for the components are put together to form a single model called a state space model which provides the basis for analysis. The book is primarily aimed at applied statisticians and econometricians. Not much of math background is needed to go through the book,at least the first part of the book.

** Your home is whatever your love more than yourself.** - Elizabeth Gilbert [youtube https://www.youtube.com/watch?v=_waBFUg_oT8?rel=0]

Via TP - Data Scientist Interviews Parham Aarabi, Founder of Modi Face ModiFace technology simulates skin-care and cosmetics products on user photos. So, a skin care product that reduces dark spots, or a shiny lipstick, or a glittery eyeshadow … we specialize in making custom simulation effects for all facial products. This is us as a core Pick problems that in your view truly matter.

Data Science is a very loose word and can mean different things in different situations. However one thing is certain, the principles used in tacking problems are from diverse fields. Drew Conway has this Venn diagram on his blog : In such a diverse field one does not know where to start and how to start. Someone has made a nice Metromap too. All said and done, this is a field that has considerable entry barriers.

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.