How Do You Prove Basic Probability Theory?

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SUMMARY

The discussion centers on the foundational aspects of proving basic probability theory, particularly in relation to how prior situations influence future probabilities. The conversation highlights the importance of understanding probability distributions, with a specific mention of the Central Limit Theorem, which asserts that the normal distribution is crucial for many probabilistic analyses. The user references Mathematica by Wolfram, indicating a need for computational tools to explore these concepts further.

PREREQUISITES
  • Understanding of basic probability concepts
  • Familiarity with probability distributions
  • Knowledge of the Central Limit Theorem
  • Experience with Mathematica by Wolfram
NEXT STEPS
  • Study introductory probability theory texts focusing on proofs of probability distributions
  • Explore the Central Limit Theorem in depth
  • Learn how to use Mathematica for probabilistic simulations
  • Investigate the implications of prior events on future probabilities
USEFUL FOR

Students of mathematics, statisticians, data scientists, and anyone interested in the foundational principles of probability theory and its applications in computational tools like Mathematica.

serin
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Hello There,
this is my first post. I would like an information: Which is the basic way to prove probability theory?.. I mean prove the probability that a situation in influenzed from the situation before and/or calculate the probability that in n it will have a determinate situation...??
How you see i want the logic.

Bye
P.s This is a problem that derivate from Mathematica of Wolfram and i have to understand how it will be my atom after n (integral number) passes..
Thanks cellular
 
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?? Then pick up an introductory probability text. I'm not going to go through all of that here since "proving that the various probability distributions really do answer the questions they are designed to answer", which is what your question really is,typically takes from 1/4 to 1/3 of an introdutory probability text. Culminating with the "Central Limit Theorem" which basically says "all you really need is the normal distribution"! And I wouldn't pretend to be qualified to give an off hand proof of that.
 

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