Відео

Hi Lawrence. I have only come across your video just now while trying to understand zipf law as it is being mentioned in my textbook in data structures. I find your video helpful. Thank you!

Sir, Could you please help me with this problem? If the cumulative distribution function is Fx(x) = 1 - e^t , where 0<=t How can I find the expected value and variance? I can't understand the term e^t

Name of the book?

The best so far....thank youuuuuuuu!

this would've been nice to know for my exam ( i was demolished)...hard af without knowing the trick here. i got to the integral and folded

I have a test tomorrow and didn't understand how to do this, this helped me a ton! Thank you!

The proof is trivial and left to reader the as an exercise….

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Alpha = {[(mean x mean) - (mean x mean x mean)] / variance} - mean Beta = {[(mean) - (2 x mean x mean) + (mean x mean x mean)] / variance} - (1 - mean) Incidentally, the Beta parameter calculation can be simplified if the Alpha parameter is calculated first as :- Beta = Alpha x [(1/mean) - 1]

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