As we all know, puppies are cute and relaxing... once they're trained (for some motivation, there's a picture of puppies at the bottom of this question!). Some day, I think I'll get a puppy. The only problem is I can't decide what I will name the puppy when I eventually get one. I've narrowed the list of potential names down to three (either Brad, Chad, or Vlad) but I seem to change my mind from one day to the next according to the following pattern: • One day l'll think to myself, "I prefer the name Brad," but then the next day I'm equally likely to switch my preference to one of the other two names. • On a different day I find myself thinking, "I prefer the name Chad," but then the day after that I switch my preference to either Brad or Vlad. In this case, I'm twice as likely to prefer Vlad. • On any day I've been thinking "I prefer Vlad" I just get so frustrated that I ask chance to decide. I roll a fair die and if it is 3 or less I switch my preference on the next day to Brad, if its 4 or 5I switch my preference to Chad on the next day, otherwise I just stick with Vlad. (A) Let p1 (t) be the probability that I prefer the name Brad on day t, let po (t) be the probability that I prefer the name Chad on day t, and let p3 (t) be the probability that I prefer the name Vlad on day t. If
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
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