Difference between revisions of "Distributions"
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Distributions give a general description of what the probabilities and events look like. The sample space, which is represented like <math>\Omega</math>, represents the set of all possible outcomes. For example, <math>\Omega =</math> {<math>1, 2, 3, 4, 5, 6</math>} would represent the sample space of rolling a die. | Distributions give a general description of what the probabilities and events look like. The sample space, which is represented like <math>\Omega</math>, represents the set of all possible outcomes. For example, <math>\Omega =</math> {<math>1, 2, 3, 4, 5, 6</math>} would represent the sample space of rolling a die. | ||
| − | The difference between Discrete and Continuous, is that discrete has a finite possible number of outcomes, and continuous | + | The difference between Discrete and Continuous, is that discrete has a finite possible number of outcomes, and continuous has an infinite number of outcomes, like measuring the weight of block of cheese, it could be <math>591.12478</math> or it could be <math>392.057721566490153286060651209008240243</math>. Discrete could be like the number rolled on a dice. |
| + | Pls finish this page. | ||
Latest revision as of 10:01, 9 July 2024
Probability distribution is a function that gives the outcome of an event, to their corresponding probabilities. For example, in rolling a fair dice, with 
sides, 
would be 
.
General Symbols/Definitions
Distributions give a general description of what the probabilities and events look like. The sample space, which is represented like 
, represents the set of all possible outcomes. For example, 
{
} would represent the sample space of rolling a die.
The difference between Discrete and Continuous, is that discrete has a finite possible number of outcomes, and continuous has an infinite number of outcomes, like measuring the weight of block of cheese, it could be 
or it could be 
. Discrete could be like the number rolled on a dice.
Pls finish this page.
