Conditional probabilities arise when you revise the probabilities previously attached to some events in order to take new information into account. The revised probabilities are 'conditional on the new information you have received'.
May - or may not - be a conditional probability. A conditional probability is not becessarily chronologically structured.
An example of conditional probability is the likelihood of drawing a red card from a standard deck of cards, given that the card drawn is a heart. Since all hearts are red, the conditional probability of drawing a red card given that it is a heart is 100%, or 1. This can be mathematically expressed as P(Red | Heart) = 1.
The conditional probability is 1/4.
They are both measures of probability.
If the events are independent then you can multiply the individual probabilities. But if they are not, you have to use conditional probabilities.
May - or may not - be a conditional probability. A conditional probability is not becessarily chronologically structured.
It can be called a "conditional probability", but the word "conditional" is irrelevant if the two events are independent.
If events A and B are statistically indepnedent, then the conditional probability of A, given that B has occurred is the same as the unconditional probability of A. In symbolic terms, Prob(A|B) = Prob(A).
The probability of event A occurring given event B has occurred is an example of conditional probability.
Tree diagram
A conditional event.
The probability that, if I get caught by a red light at one set of traffic lights, I will get a green at the next lights is an example.
The conditional probability is 1/4.
They are both measures of probability.
A conditional statement is used to show the cause for a reaction. This is an if then type of statement. The most common word that is used to signal a conditional statement is the word if.
This is a conditional probability, given the card is red, what is the chance it is a heart. Since there are 2 red hearts, the probability if 1/2
If the events are independent then you can multiply the individual probabilities. But if they are not, you have to use conditional probabilities.