**Explain BAYES' THEOREM:** Also, Bayes' Rule. An equation for calculating the probability that something is true if something potentially related to it is true. If P(A) means “the probability that A is true” and P(A|B) means “the probability that A is true if B is true,” then Bayes' Theorem tells us that P(A|B) = (P(B|A)P(A)) / P(B). This is useful for working with false positives—for example, if x% of people have a disease, the test for it is correct y% of the time, and you test positive, Bayes' Theorem helps calculate the odds that you actually have the disease. The theorem also makes it easier to update a probability based on new data, which makes it valuable in the many applications where data continues to accumulate. Named for eighteenth-century English statistician and Presbyterian minister Thomas Bayes. See also Bayesian network, prior distribution.

Different definitions in web development like **Bayes' Theorem** in Dictionary B.

- Manual Binomial Distribution:
- Meaning outcomes of independent events with two mutually exclusive possible outcomes, a fixed number of trials, and a constant probability of success. This is a discrete probability distribution, as opposed bayes' theorem definition.
- Manual Backpropagation:
- Meaning algorithm for iteratively adjusting the weights used in a neural network system. Backpropagation is often used to implement gradient descent. See also neural network, gradient descent bayes' theorem explain.
- Manual Bayesian Network:
- Meaning “Bayesian networks are graphs that compactly represent the relationship between random variables for a given problem. These graphs aid in performing reasoning or decision making in the face of bayes' theorem what is.
- Manual Big Data:
- Meaning a popular marketing buzz phrase, definitions have proliferated, but in general, it refers to the ability to work with collections of data that had been impractical before because of their volume bayes' theorem meaning.
- Manual Bias:
- Meaning “bias is a learner’s tendency to consistently learn the same wrong thing. Variance is the tendency to learn random things irrespective of the real signal.... It’s easy to avoid overfitting bayes' theorem abbreviation.