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.

Bayesian refers to a branch of statistics in which the true nature of a non-deterministic event are not known but are expressed as probabilities. These are improved as more evidence is gathered.

Robert L. Winkler has written:

'Statistics' -- subject(s): Mathematical statistics, Probabilities

'An introduction to Bayesian inference and decision' -- subject(s): Bayesian statistical decision theory

Irwin Guttman has written:

'Magnitudinal effects in the normal multivariate model' -- subject(s): Bayesian statistical decision theory, Multivariate analysis

'Theoretical considerations of the multivariate Von Mises-Fischer distribution' -- subject(s): Mathematical statistics, Multivariate analysis

'Bayesian power' -- subject(s): Bayesian statistical decision theory, Statistical hypothesis testing

'Bayesian assessment of assumptions of regression analysis' -- subject(s): Bayesian statistical decision theory, Linear models (Statistics), Regression analysis

'Linear models' -- subject(s): Linear models (Statistics)

'Bayesian method of detecting change point in regression and growth curve models' -- subject(s): Bayesian statistical decision theory, Regression analysis

'Spuriosity and outliers in circular data' -- subject(s): Outliers (Statistics)

'Introductory engineering statistics' -- subject(s): Engineering, Statistical methods

It sounds like Bayesian statistics.