A discrete uniform distribution describes a random variable that can take on a fixed number of possible values, with each value having an equal probability of occurring. For example, when rolling a six-sided die, each number from 1 to 6 is equally likely to result. The probability of any given outcome is 1 divided by the total number of possible outcomes. The Bernoulli distribution models a random variable with only two possible outcomes, like success and failure, while the binomial distribution describes repeated independent yes/no experiments, like coin flips.

1. other discrete
PROBABILITY
DISTRIBUTION
By: Angelo Genovana
1.4-

2. Discrete Uniform Distribution
A random variable has a discrete
uniform distribution where all the values
of the random variable are equally
likely, that is they have equal
probabilities.

3. If the random variable x assumes
the values x1, x2, x3... xn, that are
equally likely then it as a discrete
uniform distribution. The probability of
any outcome x, is 1/n.

4. Example:
When a far die is thrown, the
possible outcomes are 1, 2, 3, 4, 5, and
6. each time the die is thrown, it can roll
on any of these numbers. Since there
are six numbers, the probability of a
given score is 1/6. Therefore, we hav a
discrete uniform distributions.

5. •The probabilities are equal as shown
below.
P(1)= 1/6 P(2)= 1/6
P(3)= 1/6 P(4)= 1/6
P(5)= 1/6 P(6)= 1/6

6. The probabilty distributions of x is shown in
the table below, where the random variable x
represents the outcomes.
x 1 2 3 4 5 6
P(x) 1/6 1/6 1/6 1/6 1/6 1/6

7. Bernoulli Distribution
The Bernoulli distributions, named
after the Swiss mathematician Jacob
Bernoulli, is a probability distribution of
a random variable x which only two
possible outcomes. 1 and 0, that is
success and failure is q=1-p.

8. Binomial Probability Distribution
•A binomial experiment possesses the
following properties:
-the experiment conduct of a
repeated trials
-each trials is independent of the
previous trials