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Gwerth Probablitiy
They are both estimates of the probability of outcomes that are of interest. Experimental probabilities are derived by repeating the experiment a large number of times to arrive at these estimates whereas theoretical probabilities are estimates based on a mathematical model based on some assumptions.
(1) That the probabilities lie between 0 and 1. (2) The sum of all probabilities of the distribution sum up to 1.
Sum of all probabilities is 1.
The sum of the probabilities of all possible outcomes is 1.
Gwerth Probablitiy
assumption is objective and personal judgment is subjective
Empirical Distribution: based on measurements that are actually taken on a variable. Theoretical Distribution: not constructed on measurements but rather by making assumptions and representing these assumptions mathematically.
They are both estimates of the probability of outcomes that are of interest. Experimental probabilities are derived by repeating the experiment a large number of times to arrive at these estimates whereas theoretical probabilities are estimates based on a mathematical model based on some assumptions.
empirical probability is when you actually experiment with it and get data values, and theoretical probability is when you use math to make an educated guess.
The axioms are the initial assumptions. The theorems are derived, by logical reasoning, from the axioms - or from other, previously derived, theorems.
I think most people use them as synonyms. In general usage, it can be appropriate. However, a probabilistic approach describes the occurrence of deterministic states with given probabilities, while stochastic processes are built up by sequential steps occurring with given probabilities. Think of the difference between throwing a die once which determines the state you will arrive at and throwing a die multiple times where the resulting states are (can be) dependent on the previous states.
(1) That the probabilities lie between 0 and 1. (2) The sum of all probabilities of the distribution sum up to 1.
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.
A. A. Markov has written: 'Differenzenrechnung' -- subject(s): Difference equations, Interpolation 'The Correspondence between A.A. Markov and A.A. Chuprov on the theory of Probability and Mathematical statistics' -- subject(s): Correspondence, Mathematical statistics, Mathematicians, Probabilities '[Izbrannye trudy' -- subject(s): Bibliography, Number theory, Probabilities
0- less than1
No. Probabilities are 'counted' as between 1 (certain) and 0 (impossible)