Equality in conditional probability

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Discussion Overview

The discussion revolves around the equality of two conditional probability expressions, specifically in the context of Dudas Pattern Classification. Participants are seeking clarification on the definitions and justifications for the expressions P(x,θ|D) and P(x|θ,D), exploring theoretical aspects of probability and its notation.

Discussion Character

  • Exploratory, Technical explanation, Conceptual clarification

Main Points Raised

  • Some participants question the justification for the equality P(x,θ|D) = P(x|θ,D)P(θ|D) as stated in Dudas Pattern Classification.
  • There is a request for clarification on the definitions of P(x,θ|D) and P(x|θ,D), with one participant noting their lack of familiarity with the notation despite a background in probability and statistics.
  • One participant provides definitions, stating that P(x,θ|D) represents the probability of x and θ given D, while P(x|θ,D) is the probability of x given D and θ, identifying x as a random variable and θ as a parameter being estimated.
  • A suggestion is made to start the analysis using Bayes' rule to explore the relationship between the probabilities.

Areas of Agreement / Disagreement

Participants do not appear to reach a consensus on the justification for the equality, and multiple viewpoints regarding the definitions and interpretations of the probabilities remain present.

Contextual Notes

Participants express uncertainty regarding the notation and definitions, indicating a potential gap in understanding or familiarity with the material discussed.

Avatrin
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Hi

In Dudas Pattern Classification, he Writes that [itex]P(x,\theta|D)[/itex] can always be written as [itex]P(x|\theta,D)P(\theta|D)[/itex]. However, I cannot find any justification for this. So, why are these Equal?
 
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Avatrin said:
Hi

In Dudas Pattern Classification, he Writes that [itex]P(x,\theta|D)[/itex] can always be written as [itex]P(x|\theta,D)P(\theta|D)[/itex]. However, I cannot find any justification for this. So, why are these Equal?
How are ##P(x,\theta|D)## and ##P(x|\theta,D)## defined? I took a lot of classes in probability and mathematical statistics, but it has been many years ago. I don't recall seeing this notation.
 
Mark44 said:
How are ##P(x,\theta|D)## and ##P(x|\theta,D)## defined? I took a lot of classes in probability and mathematical statistics, but it has been many years ago. I don't recall seeing this notation.

[itex]P(x,\theta|D)[/itex] is the probability of [itex]x[/itex] and [itex]\theta[/itex] given [itex]D[/itex] which is our sample set. [itex]P(x|\theta,D)[/itex] is the probability of [itex]x[/itex] given [itex]D[/itex] and [itex]\theta[/itex]. [itex]x[/itex] is a random variable, and [itex]\theta[/itex] is a parameter which we are estimating by considering it to be a random variable.
 
Start by writing ##P(x,\theta/D)## using Bayes rule.
 

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