# Bayesian probability

#### Pat494

##### Legendary member
14,593 1,564
Bayesian probability is one of the different interpretations of the concept of probability and belongs to the category of evidential probabilities. The Bayesian interpretation of probability can be seen as an extension of the branch of mathematical logic known as propositional logic that enables reasoning with propositions whose truth or falsity is uncertain. To evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data.

The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. Bayesian probability interprets the concept of probability as "an abstract concept, a quantity that we assign theoretically, for the purpose of representing a state of knowledge, or that we calculate from previously assigned probabilities," in contrast to interpreting it as a frequency or "propensity" of some phenomenon.

The term "Bayesian" refers to the 18th century mathematician and theologian Thomas Bayes, who provided the first mathematical treatment of a non-trivial problem of Bayesian inference. Nevertheless, it was the French mathematician Pierre-Simon Laplace who pioneered and popularised what is now called Bayesian probability.

Broadly speaking, there are two views on Bayesian probability that interpret the probability concept in different ways. According to the objectivist view, the rules of Bayesian statistics can be justified by requirements of rationality and consistency and interpreted as an extension of logic. According to the subjectivist view, probability quantifies a "personal belief". Many modern machine learning methods are based on objectivist Bayesian principles. In the Bayesian view, a probability is assigned to a hypothesis, whereas under the frequentist view, a hypothesis is typically tested without being assigned a probability.

Wikipedia

This looks like a complicated field of maths that should be useful for forecasting ?

##### Legendary member
10,850 1,233
Bayesian probability is one of the different interpretations of the concept of probability and belongs to the category of evidential probabilities. The Bayesian interpretation of probability can be seen as an extension of the branch of mathematical logic known as propositional logic that enables reasoning with propositions whose truth or falsity is uncertain. To evaluate the probability of a hypothesis, the Bayesian probabilist specifies some prior probability, which is then updated in the light of new, relevant data.

The Bayesian interpretation provides a standard set of procedures and formulae to perform this calculation. Bayesian probability interprets the concept of probability as "an abstract concept, a quantity that we assign theoretically, for the purpose of representing a state of knowledge, or that we calculate from previously assigned probabilities," in contrast to interpreting it as a frequency or "propensity" of some phenomenon.

The term "Bayesian" refers to the 18th century mathematician and theologian Thomas Bayes, who provided the first mathematical treatment of a non-trivial problem of Bayesian inference. Nevertheless, it was the French mathematician Pierre-Simon Laplace who pioneered and popularised what is now called Bayesian probability.

Broadly speaking, there are two views on Bayesian probability that interpret the probability concept in different ways. According to the objectivist view, the rules of Bayesian statistics can be justified by requirements of rationality and consistency and interpreted as an extension of logic. According to the subjectivist view, probability quantifies a "personal belief". Many modern machine learning methods are based on objectivist Bayesian principles. In the Bayesian view, a probability is assigned to a hypothesis, whereas under the frequentist view, a hypothesis is typically tested without being assigned a probability.

Wikipedia

This looks like a complicated field of maths that should be useful for forecasting ?

You have to understand it, first, though.

#### Pat494

##### Legendary member
14,593 1,564
You have to understand it, first, though.

That's the difficult bit. Let's hope someone codes it up for us thicko s

#### scose-no-doubt

##### Veteren member
4,630 954
- teh scose has completed an MSc level QMIM module and he believes that a Bayesian approach and it's validity would depend entirely on your time frame.

I'm out

#### Shakone

##### Senior member
2,458 665
- teh scose has completed an MSc level QMIM module and he believes that a Bayesian approach and it's validity would depend entirely on your time frame.

I'm out
]

Why? Bayesian approach doesn't care about timeframe, just data.

#### scose-no-doubt

##### Veteren member
4,630 954
I meant like in a time horizon way... Timeframe was the wrong word lol

357 209

#### scose-no-doubt

##### Veteren member
4,630 954
I meant like in a time horizon way... Timeframe was the wrong word lol

S'gonna be bias depending on when my man starts da ting innit

#### blueraincap

##### Newbie
5 0
S'gonna be bias depending on when my man starts da ting innit

:clap: