Generic algorithm for probability any propositional formula

In summary, the conversation discusses the possibility of a generic algorithm to compute the probability of any propositional formula, given the probabilities of its individual atoms. The suggestion is to repeatedly apply Bayes' rule, but it may be difficult to obtain all the required probabilities. The concept of independence is mentioned as a simplifying factor. The conversation ends with a request for an algorithm or reference, to which the response is that any programming language can be used to write an algorithm and general definitions of the term "algorithm" are provided.
  • #1
Aldebaran2
6
0
Ciao all,

Is there a generic algorithm to compile the probability P(A)
of any propositional formula A (provided that we have only the probability of each atoms constituting A)?

For example, we have that A= a1 and not (a2 and a3 and ( not a4 and not a5 and not a6 ..) , or any other complicated formula.
The an are independant here!

I know pr(a1), pr(a2)... and so on...

Is there an algorithm to compute pr(a1 and not (a2 and a3 and ( not a4 and not a5 and not a6 ..) ) ?
 
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  • #2
Just repeatedly apply Bayes' rule. But you will have difficulty usually coming up with numbers for all of the required terms.
 
  • #3
Applying Bayes.

Thank you for the advice! I shall try it.

What did you exactly mean by:

"you will have difficulty usually coming up with numbers for all of the required terms"


?
 
  • #4
Essentially you'll need all the cross probabilities -- all the and's and or's of the all the possible pairs of propositions. Usually, these numbers aren't easy to come up with.
 
  • #5
I do not get it.

Could you provide an example?

or do you have a reference where that is well explained ( I am not an expert of Probabiliies)?
 
  • #6
Any standard probabilities textbook would be a good place to start. My personal favourite is Probability Theory by E.T. Jaynes.
 
  • #7
Well, if the a's are independent, that simplifies the problem considerably.
 
  • #8
Ciao EnumaElish,

Do you have an algorithm? eventually a reference to it?
 
  • #9
Aldebaran2 said:
Ciao EnumaElish,

Do you have an algorithm? eventually a reference to it?

I'm fairly sure nothing beyond a textbook would even bother to spell it out:

given two independent events, A and B, pr(A and B) = pr(A) pr(B), pr(A or B) = pr(A) + pr(B). Generalise in the obvious way for more than 2 events.
 
  • #10
Last edited:

1. What is a generic algorithm for probability any propositional formula?

A generic algorithm for probability any propositional formula is a step-by-step process that can be applied to any propositional formula in order to calculate its probability. It involves breaking down the formula into its constituent parts and using probability rules and laws to determine the overall probability.

2. How does the generic algorithm handle complex propositional formulas?

The generic algorithm is designed to handle complex propositional formulas by breaking them down into simpler parts and applying probability rules and laws to each part. This allows for the overall probability to be calculated in a systematic and efficient manner.

3. Can the generic algorithm be applied to any type of propositional formula?

Yes, the generic algorithm can be applied to any type of propositional formula as long as it follows the basic rules and laws of probability. This includes formulas with multiple variables, negations, and logical connectives.

4. How accurate is the generic algorithm in calculating probabilities?

The accuracy of the generic algorithm depends on the accuracy of the individual probability calculations and the complexity of the formula. In general, it is a reliable method for calculating probabilities, but it may not be 100% accurate in all cases.

5. Is the generic algorithm a commonly used method in probability calculations?

Yes, the generic algorithm is a commonly used method in probability calculations, especially in the field of computer science and artificial intelligence. It provides a systematic and efficient approach to calculating probabilities for complex propositional formulas.

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