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A Markov blanket of a random variable in a random variable set = {, …,} is any subset of , conditioned on which other variables are independent with : . It means that contains at least all the information one needs to infer , where the variables in are redundant.
Code Year was a free incentive Codecademy program intended to help people follow through on a New Year's Resolution to learn how to program, by introducing a new course for every week in 2012. [32] Over 450,000 people took courses in 2012, [33] [34] and Codecademy continued the program into 2013. Even though the course is still available, the ...
The Zen of Python is a collection of 19 "guiding principles" for writing computer programs that influence the design of the Python programming language. [1] Python code that aligns with these principles is often referred to as "Pythonic". [2] Software engineer Tim Peters wrote this set of principles and posted it on the Python mailing list in ...
When variable decelerations are associated with uterine contractions, their onset, depth, and duration commonly vary with successive uterine contractions. [citation needed] Prolonged deceleration: a decrease in FHR from baseline of at least 15 bpm, lasting at least 2 minutes but less than 10 minutes. A deceleration of at least 10 minutes is a ...
A nonstress test can be classified as normal, atypical, or abnormal. A normal nonstress test will show a baseline fetal heart rate between 110 and 160 beats per minute with moderate variability (5- to 25-interbeat variability) and 2 qualifying accelerations in 20 minutes with no decelerations.
In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution.Given a probability distribution, one can construct a Markov chain whose elements' distribution approximates it – that is, the Markov chain's equilibrium distribution matches the target distribution.
Filter feature selection is a specific case of a more general paradigm called structure learning.Feature selection finds the relevant feature set for a specific target variable whereas structure learning finds the relationships between all the variables, usually by expressing these relationships as a graph.
For a supersonic flow in an expanding conduit (M > 1 and dA > 0), the flow is accelerating (dV > 0). For a supersonic flow in a converging conduit (M > 1 and dA < 0), the flow is decelerating (dV < 0). At a throat where dA = 0, either M = 1 or dV = 0 (the flow could be accelerating through M = 1, or it may reach a velocity such that dV = 0).