- Course Introduction
- 01: Evidence & DNA
- 02: Forensic Biology
- 03: DNA Extraction & Quantitation
- 04: DNA Amplification
- 05: Amplified DNA Product Separation
- 06: STR Data Analysis & Interpretation
- 07: Population Genetics & Statistics
- 08: Communicating Results
- 09: Other DNA Markers & Technologies
- Course Resources
- Laboratory Training Manual
President's DNA Initiative
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Advancing Justice Through DNA Technology
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Advancing Justice Through DNA Technology
DNA Analyst Training
Logical Approach
Home > Population Genetics & Statistics > Statistics > Logical Approach
Bayes Theorem
Many statisticians have employed what is known as Bayesian probability, after the British clergyman Thomas Bayes, which is based on probability as a measure of one’s degree of belief.01 This type of probability is conditional in that the outcome is based on knowing information about other circumstances and is derived from Bayes Theorem, published in 1764.14
Click here to read more about Bayes Thereom.
Conditional Probability
Conditional probability, by definition, is the probability P of an event A given that an event B has occurred.15 Mathematically, conditional probabilities can be represented as: P(A | B) where “|” means “given that”.
Example: |
Take the example of a die with six sides. If one was to throw the die, the probability of it landing on any one side would be 1/6. This probability, however, assumes that the die is not weighted or rigged in any way, and that all of the sides contain a different number. If this were not true, then the probability would be conditional and dependent on these other factors. |
