--- --- restore content begins
Biostatistics
Classical probability type:
Theoretically, the test results may not previously obtained according to the experimental conditions, all previously estimated results may be referred to as a sample space, is the set Ω. Sample point w is an element of Ω. This is the classical definition of probability, that is, according to the characteristics of the event itself, directly probabilities. It get here is often a priori probabilities.
Is a collection of random events, is a subset of the sample space.
The event is bound to a collection that contains all sample points.
Is a collection of unlikely events, does not contain all sample points.
Today：
Different definitions and classical probability, and now we know that the frequency of thing has happened, and by Bernoulli's law of large numbers that the frequency of a large sample of approximately equal probability, been here often posterior probability. After that the sample distribution, the use of probability theory to make inferences, namely Prediction .
Under the premise of a known probability of mathematical probabilities calculated probability of an event you want to happen is probability theory.
The addition and multiplication formula comprehensive formula using the resulting total probability formula and Bayes' formula :
A total probability formula reasons (usually known A probability at B occurrence probability + A probability of occurrence) to push the result
Bayesian formula: Reason A cause event B occurs; the result (an event has occurred under a conditional probability) to push the cause (an event has occurred, it is the probability of certain causes), often know the results.
Random Variables:
There are two types :
Discrete variables:
Distribution Ratio: When the variable is a corresponding occurrence probability of a single value;
Occurrence probability corresponding single value when certain variables are: the distribution function
Continuous variables:
When there is a corresponding variable is a certain probability value: probability density curve
Corresponding occurrence probability is a certain value when the variable: distribution function
Mathematical formulas may be calculated using the above parameters.
Common probability distributions:
Binomial the n- , Poisson lot of time close to the normal distribution
Poisson distribution: Rare Event
Normal distribution:
Significance level: 0.05
Khintchine law of large numbers: when the number of samples tends to infinity, and the parameter means is equal to the sample mean
--- end --- restore content