there are 3 types of conditional probability:
1. the indicate: if antecedent happens, then evidence manifests itself
example -> when tossing a coin, if it lands on tails, then you win the game
2. the subjunctive: if antecedent would happen, then evidence would manifest itself
example -> when a coin was tossed, if it would have landed on tails, i would have won the game
it is recommended for optimal bayesian inferences that your a priori distribution is indicative. If not, you could be dealing with inproper, uninformative, or hyper priors, which make decision-making and posterior determination more complex, if even possible.
Posterior distributions could very well be subjunctive. Suppose i have won the game, i could have tossed tails, but i could also have tossed heads.
The probability of event A occurring given event B has occurred is an example of conditional probability.
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).
Tree diagram
A conditional event.
Probability is the chance of an event occurring. For example when flipping a coin you have a 50% chance that it will land on heads and a 50% chance that it will land on tails since there are only two possibilities.Conditional probability refers to when one event is dependent on another event occurring. It can also be written as the probability of an event B occurring after event A has already occurred. The notation for conditional probability is P(B|A). (Note: this does not mean B divided by A but probability of B after A)When two events are dependent, the probability of them both occurring is:P(A and B)=P(A)P(B|A)So for example: 53% of residents have home owners insurance. Of them, 27% has auto-insurance. If a resident is selected at random, what is the probability they with have both insurances?Let H stand for home owners insurance = 53% or 0.53Let A stand for auto insurance = 27% or 0.27P(H and P)=P(H)P(A/H)=(0.53)(0.27)= 0.1431So the probability of residents have both home owners and auto insurance is 0.1431 or 14.31%
The probability of event A occurring given event B has occurred is an example of conditional probability.
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.
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.
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'.
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).
What are conditional connectives? Explain use of conditional connectives with an example
Tree diagram
A conditional event.
The conditional probability is 1/4.
Not enogh information to answer. Define"conditional discharge." Discharge from WHAT?
probability