Binomial Random Variable

AKA: Binary/Boolean/Bernoulli RV.
Context:
- Input: a Non-Negative Integer (for the number of Trials).
- Output: a Non-Negative Integer (for the number of Successes).
- It can (typically represents a Binomial Process (such as a coin toss).
- It can be represented by a Binary Probability Function.
- It can range from being a Boolean Random Variable (true, false) to being (an Ordinal Binary Random Variable (such as 1, -1).
Example(s):
- X(10)=0.
- X(10)=5.
- One that describes the event that John has cancer which can take a value of 1 (John has cancer) or 0 (John does not have cancer).
- …
Counter-Example(s):
- a Multinomial Random Variable.
- a Continuous Random Variable.
- a Binary Variable.
See: Binary Dependent Variable, Binary Outcome, Binary Target Attribute, Probabilistic Reasoning, Probabilistic Inference.

References

http://deepdive.stanford.edu/inference
- QUOTE: Probabilistic inference is the task of deriving the probability of one or more random variables taking a specific value or set of values. For example, a Bernoulli (Boolean) random variable may describe the event that John has cancer. Such a variable could take a value of 1 (John has cancer) or 0 (John does not have cancer). DeepDive uses probabilistic inference to estimate the probability that the random variable takes value 1: a probability of 0.78 would mean that John is 78% likely to have cancer.