Wikipedia has a goodįor information, check it out for more details. Now let's start from the basic idea of information. So everything we talk about is with respect to a probabilistic That the "information" (of a data source) is modelled as a probabilityĭistribution. "information" part refers to information theory, which deals with sending It's probably best to initially to treat them as separate things. There are parallels, and connections have been made between the two ideas, but First, informationĮntropy is a distinct idea from the physics concept of thermodynamic entropy. Let's first clarify two important points about terminology. Piece together a picture that you can internalize. Sense to you, I encourage you to find a few different sources until you can I'll try to describe one that makes sense to me. There are plenty of ways to intuitively understand information entropy, Information Entropy and Differential Entropy Some intuition, some math, and some examples. Natural language processing applications with maximum entropy (Ma圎nt)Ĭlassifiers (i.e. Inference to determine prior distributions and also (at least implicitly) in As you may have guessed, this is used often in Bayesian the mean), you can find theĭistribution that makes the fewest assumptions about your data (the one with maximal Using the principle of maximumĮntropy and some testable information (e.g. This post will talk about a method to find the probability distribution that bestįits your given state of knowledge.
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