From the user's perspective, MDP is a collection of supervised and unsupervised learning algorithms and other data processing units that can be combined into data processing sequences and more complex feed-forward network architectures.
This is code implements the example given in pages 11-15 of An Introduction to the Kalman Filter by Greg Welch and Gary Bishop, University of North Carolina at Chapel Hill, Department of Computer Science.
Technical Note: The Financial Forecast Center has moved away from publishing standard deviations of the forecast's performance in recognition that the distribution of value movements in the financial markets follow Levy or Cauchy distributions, not Gaussian or normal distributions. Likewise, the forecast model's errors follow similar distributions. A Gaussian distribution significantly underestimates the probability of a large price or rate movement. A Gaussian distribution may underestimate the probabilty of a 3 sigma price movement by a factor of 10. In other words, the chance of a 3 sigma movement is potentially 10 times greater than that predicted by a Gaussian probability curve. The above change in error reporting enables a more accurate depiction of a forecast model's potential performance.
The purpose of Data.gov is to increase public access to high value, machine readable datasets generated by the Executive Branch of the Federal Government.