prioirandposterior dist
A posterior probability is the probability of assigning observations to groups given the data. A prior probability is the probability that an observation will fall into a group before you collect the data. For example, if you are classifying the buyers of a specific car, you might already know that 60% of purchasers are male and 40% are female. If you know or can estimate these probabilities, a discriminant analysis can use these prior probabilities in calculating the posterior probabilities. When you don't specify prior probabilities, Minitab assumes that the groups are equally likely.
The probability of a boy is still 0.5 no matter how many prior children there are.
a fair and probability
When you throw a die, there are six possibilities. The probability of a number from 1 to 6 is 1/6. This is classical probability. Compare this with empirical probability. If you throw a die 100 times and obtain 30 sixes, the probability of obtaining a 6 is 30/100 or 0.3. Empirical probabilities change whereas classical probability doesn't.
Subjective If you assume particular events will happen with a certain prior distribution, that is Bayesian probability.
An independent probability is a probability that is not based on any other event.An example of an independent probability is a coin toss. Each toss is independent, i.e. not related to, any prior coin toss.An example of a dependent probability is the probability of drawing a second Ace from a deck of cards. The probability of the second Ace is dependent on whether or not a first Ace was drawn or not. (You can generalize this to any two cards because the sample space for the first card is 52, while the sample space for the second card is 51.)
One prerequisite for Bayesian statistics is that you need to know or have prior knowledge of the opposite of the probability you are trying to create.
The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.The complement (not compliment) of the probability of event A is 1 minus the probability of A: that is, it is the probability of A not happening or "not-A" happening.
The probability is 0.The probability is 0.The probability is 0.The probability is 0.
1/2 apex It does not matter what each prior flip's result was. Each flip has a probability of 0.5 heads or tails. Coins do not have "memory".
No 1.001 is not a probability. Probability can not be >1