Difference between Bayesian & Modern Probability

In summary, the article does not accurately describe what Bayesian Probability is. Bayesian Probability is a specific type of probability that is used in mathematical theories. It is different from the normal probability that we study at University, and it is a topic that is explored in more advanced courses.
  • #1
woundedtiger4
188
0
Hi all,

What is the difference between Bayesian Probability
http://en.wikipedia.org/wiki/Bayesian_probability

and the normal probability that we study at University, isn't Bayesian Probability simply the conditional probability that we study in Probability & Measure or in any other text of probability?

Thanks in advance.
 
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  • #2
I suggest you consult other sources. That article is isn't well written.

You have to distinguish between at least 3 different subjects. There are

1) The Mathematical theory of probability

2) Different ways of posing real life problems as problems of mathematicas - e.g. "Bayesian" vs "Frequentist" statistics

3) Philosophical ideas about what probability means.

The Wikipedia article appears to be about 3), the philosophical or metaphysical interpretation of probability. According to E.T. Jaynes, there are thousands of different Bayesian interpretations of probability.

The major philosophical interpretations of probability don't disagree on the mathematical laws of probability. There have been other sets of axioms proposed for theories of probability and there are "theories of evidence" (such as Dempster-Schafer) that are more general ideas than probability. But when you say a mathematician is a "Bayesian", it usually refers to subject 2) - i.e. to a style of approaching statistical problems.

The probability theory you learn in introductory university courses isn't contradicted by Bayesian methods introduced in more advanced courses. Whether it is contradicted by anything taught in the Philosophy Department, who's to say? You'll have to ask philosophers.
 
  • #3
Stephen Tashi said:
I suggest you consult other sources. That article is isn't well written.

You have to distinguish between at least 3 different subjects. There are

1) The Mathematical theory of probability

2) Different ways of posing real life problems as problems of mathematicas - e.g. "Bayesian" vs "Frequentist" statistics

3) Philosophical ideas about what probability means.

The Wikipedia article appears to be about 3), the philosophical or metaphysical interpretation of probability. According to E.T. Jaynes, there are thousands of different Bayesian interpretations of probability.

The major philosophical interpretations of probability don't disagree on the mathematical laws of probability. There have been other sets of axioms proposed for theories of probability and there are "theories of evidence" (such as Dempster-Schafer) that are more general ideas than probability. But when you say a mathematician is a "Bayesian", it usually refers to subject 2) - i.e. to a style of approaching statistical problems.

The probability theory you learn in introductory university courses isn't contradicted by Bayesian methods introduced in more advanced courses. Whether it is contradicted by anything taught in the Philosophy Department, who's to say? You'll have to ask philosophers.

OKKKKKKK

Sir, thank you very much.
 

FAQ: Difference between Bayesian & Modern Probability

What is the difference between Bayesian and Modern Probability?

Bayesian probability is a type of probability that takes into account prior knowledge or beliefs about an event, while modern probability is based on objective measurements and observations. In Bayesian probability, prior beliefs are updated based on new evidence, whereas modern probability does not consider prior beliefs.

How are Bayesian and Modern Probability used in practice?

Bayesian probability is often used in situations where there is limited data or where prior knowledge is important, such as in medical diagnosis or financial forecasting. Modern probability is used in scenarios where there is a large amount of data and objective measurements are more important, such as in weather forecasting or gambling.

Which approach is more accurate, Bayesian or Modern Probability?

Both Bayesian and Modern Probability have their strengths and weaknesses, and the accuracy of each approach depends on the specific situation and the quality of the data. In general, Bayesian probability may be more accurate when there is limited data or when prior beliefs are strong, while modern probability may be more accurate when there is a large amount of data available.

What are the main criticisms of Bayesian and Modern Probability?

Critics of Bayesian probability argue that it is subjective and relies too heavily on prior beliefs, which may not be accurate or representative. Critics of modern probability argue that it can be overly simplistic and may not take into account important factors that cannot be measured.

Can Bayesian and Modern Probability be used together?

Yes, Bayesian and Modern Probability can be used together in what is called Bayesian statistics. This approach combines the strengths of both methods, using prior beliefs and objective measurements to make more accurate predictions and decisions.

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