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... In this tutorial we\'ll begin by reviewing Markov Models (aka Markov Chains) and then...we\'ll hide them! This simulates a very common phenomenon... there is some underlying dynamic system running along according to simple and uncertain dynamics, but we can\'t see it. All we can see are some noisy signals arising from the underlying system. From those noisy observations we want to do things like predict the most likely underlying system state, or the time history of states, or the likelihood of the next observation. This has applications in fault diagnosis, robot localization, computational biology, speech understanding and many other areas. In the tutorial we will describe how to happily play with the mostly harmless math surrounding HMMs and how to use a heart-warming, and simple-to-implement, ... [full article] (**83** views)

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... In probability theory and statistics, a probability distribution identifies either the probability of each value of a random variable (when the variable is discrete), or the probability of the value falling within a particular interval (when the variable is continuous).[1] The probability distribution describes the range of possible values that a random variable can attain and the probability that the value of the random variable is within any (measurable) subset of that range. ... [full article] (**78** views)

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