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Bernoulli Distribution
            The Bernoulli distribution is one of the simplest discrete probability distributions. It describes events that have
            exactly two outcomes. Let us take the example of tossing an impartial coin. Either Head or Tail will appear. The
            result will only be 0 (failure) or 1 (success) if we prioritise one of them above the other (success). An impartial coin
            has a probability of 0.5 for each outcome. Always assume a binary outcome: true or false, head or tail, success or
            failure, etc.

            The probability mass function or PMF of Bernoulli Distribution is given as
              P(n) = pn ×(1–p) 1–n

            The expected value of the distribution can be derived by taking a weighted average of possible outcomes:

              E(x) = p × 1 + (1–p) × 0
            The following graph shows the Bernoulli distribution:

                          0.8

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                          0.5

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                          0.2

                          0.1

                            0
                                             S    s s e c c u                        e r u l i a F
            Binomial Distribution

            A Binomial distribution is a common probability distribution that models the probability of obtaining one of two
            outcomes under a given number of parameters. It totals the number of trials when each trial has an equal chance
            of producing a particular result. By dividing the quantity of independent trials by the number of successful trials,
            one can calculate the value of a binomial. For the trials we are looking at, the probability of receiving a success in
            a binomial distribution must stay constant. Since there are only two possible outcomes when tossing a coin, for
            instance, the probability of flipping a coin is 1/2 or 0.5 for each experiment we conduct.
            There are four different criteria of binomial distributions described below which the binomial distributions need to
            fulfil. These are as follows:
            1.  The number of the trials or the experiments must be fixed. As you can only figure out the probable chance of
              occurrence of success in a trial you should have a finite number of trials.
            2.  Every trial is independent. None of your trials should affect the possibility of the next trial.

            3.  The  probability  always  stays  the  same  and  equal.  The  probability  of  success  may  be  equal  for  more  than
              one trial.
            4. There are only two mutually exclusive outcomes, i.e., success or failure.







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