I was working with my friend on a Random Number Generator (RNG) research paper. We need to evaluate the quality of our random bitstream result using the NIST SP800-90B entropy test.<\/p>\n\n\n\n\n\n\n\n
Since this is my first time working in the RNG field, I tried to gather all information about evaluating the quality of an RNG. The SP800-90B standard is the starting point. I tried to read it again and again since there is so much jargon, one of them that took me a long time to understand is the Independent and Identically Distributed (IID). I got the concept right but each article seems to explain different complicated cases and examples which made me confuse even more. Eventually, I end up with this explanation:<\/p>\n\n\n\n
Independent is when a value is not affected by the other value.
For example, if you roll two dice, the result does not depend on each other. <\/p>Identically Distributed is when the probability of any specific outcome is the same.
For example, if you flip a coin, you had 50 50 chances of getting head or tail. That value doesn’t change every time you flip a coin. On the other hand, if you have a collection of weighted coins, where each coin had a different probability of head or tail, that would be not identically distributed. <\/p>In machine learning, IID often implies that all of the data from the training set comes from the same process and that data is not related to each other.<\/p>Terms: Independent and Identically Distributed (IID) by IntuitiveML (https:\/\/www.youtube.com\/watch?v=EGKbPww2_rc)<\/cite><\/blockquote>\n\n\n\n
Now I can sleep well.<\/p>\n","protected":false},"excerpt":{"rendered":"
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