Basic notation (conditional probability delim in linear equation)

In summary: Thanks for clarifying that.In summary, the author is using a delimiter other than comma to separate arguments in a function.
  • #1
dspiegel
3
0
Hey all.

Looking at "Pattern Recognition and Machine Learning" (Bishop, 2006) p28-31, the author appears to be using what would ordinarily be a delimiter for a conditional probability inside a linear function. See the first variable in normpdf as below. This is in the context of defining a Bayesian prior distribution over polynomial coefficients in a curve fitting problem.

[tex]p(\textbf{w} | \alpha) = NormPDF(\textbf{w} | \textbf{0}, \alpha^{-1}\textbf{I}) = \left(\frac{\alpha}{2\pi}\right)^{(M+1)/2} exp \left(-\frac{\alpha}{2}\textbf{w}^T\textbf{w}\right)[/tex]

Can anybody shine some light on this for me please?

Many thanks.
 
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  • #2
I don't know if this is precisely the case here, but sometimes delimiters other than comma are used in functions. I have mostly seen semicolons (;) and vertical bars (|).
Often this is done to separate arguments by meaning. For example, an author may write
Consider a normal distribution with mean [itex]\mu[/itex] and standard deviation [itex]\sigma[/itex]. We define the probability of finding a value between a and b as [tex]P(a, b \mid \mu, \sigma)[/tex] as ...
You can just as well write [tex]P(x, \mu, \sigma)[/tex]. However, writing a separate delimiter hopefully makes it more clear to the reader that a and b are really the variables here and, though technically mu and sigma are variables as well, in this case they are more like parameters that have been previously fixed (some arbitrary values for some normal distribution we are interested in).
 
  • #3
CompuChip said:
I don't know if this is precisely the case here, but sometimes delimiters other than comma are used in functions. I have mostly seen semicolons (;) and vertical bars (|).
Often this is done to separate arguments by meaning. For example, an author may write

You can just as well write [tex]P(x, \mu, \sigma)[/tex]. However, writing a separate delimiter hopefully makes it more clear to the reader that a and b are really the variables here and, though technically mu and sigma are variables as well, in this case they are more like parameters that have been previously fixed (some arbitrary values for some normal distribution we are interested in).

Thanks for your reply.

Although I am quite sure that's not the case in this particular instance, in general, I know non-variable parameters may be written after a semicolon.

I believe the case to be that it reads as, "the value of [tex]t_n[/tex] evaluated for [tex]y(x_n,\textbf{w})[/tex]" as described on http://en.wikipedia.org/wiki/Vertical_bar#Mathematics".

Elsewhere the likelihood of the parameters [tex]\{w,\beta\}[/tex] is written for two i.i.d. variables [tex]\{\textbf{x,t}\}[/tex] where the function y(x,w) computes the predicted value of t.

[tex]p(\textbf{t}|\textbf{x},w,\beta) = \prod_{n=1}^N NormPDF(t_n|y(x_n, \textbf{w}),\beta^{-1})[/tex]

2sLvo.png


So it seams a reasonable interpretation in this context.
 
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  • #4
dspiegel said:
Hey all.

This is in the context of defining a Bayesian prior distribution over polynomial coefficients in a curve fitting problem.

[tex]p(\textbf{w} | \alpha) = NormPDF(\textbf{w} | \textbf{0}, \alpha^{-1}\textbf{I}) = \left(\frac{\alpha}{2\pi}\right)^{(M+1)/2} exp \left(-\frac{\alpha}{2}\textbf{w}^T\textbf{w}\right)[/tex]

Can anybody shine some light on this for me please?

Many thanks.

I don't know what this is. The Bayesian expression for the conditional probability p(w|a) is:

p(w|a)=p(a|w)p(w)/p(a).
 
  • #5
SW VandeCarr said:
I don't know what this is. The Bayesian expression for the conditional probability p(w|a) is:

p(w|a)=p(a|w)p(w)/p(a).

Well there're a bit more to it. The formula you quoted is just for the prior.

The derivation is thus.

[tex]p(w|x,t,\alpha,\beta)[/tex] = (likelihood * prior) / marginal likelihood

[tex]p(w|x,t,\alpha,\beta) \propto p(t|x,w,\beta) * p(w|\alpha)[/tex]

[tex]\{\alpha,\beta\}[/tex] are hyperparameters.
 
  • #6
dspiegel said:
Well there're a bit more to it. The formula you quoted is just for the prior.

The derivation is thus.

[tex]p(w|x,t,\alpha,\beta)[/tex] = (likelihood * prior) / marginal likelihood

[tex]p(w|x,t,\alpha,\beta) \propto p(t|x,w,\beta) * p(w|\alpha)[/tex]

[tex]\{\alpha,\beta\}[/tex] are hyperparameters.

OK. I was going by the original equation where the left side was simply [tex]p([/tex]w[tex]|\alpha)=[/tex]
 

FAQ: Basic notation (conditional probability delim in linear equation)

What is basic notation in conditional probability?

Basic notation in conditional probability refers to the symbols and mathematical expressions used to represent the likelihood of an event occurring given the knowledge of another event. It is typically denoted as P(A|B), where A represents the event and B represents the condition.

How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the intersection of two events (P(A∩B)) by the probability of the condition (P(B)). This can also be written as P(A|B) = P(A∩B) / P(B).

What is the role of the delimiter in conditional probability notation?

The delimiter (|) in conditional probability notation serves as a way to separate the event from the condition. It indicates that the probability is being calculated given the condition.

How is basic notation used in linear equations?

In linear equations, basic notation is used to represent the slope (m) and y-intercept (b). The equation is typically written as y = mx + b, where m is the slope and b is the y-intercept.

What is the significance of conditional probability in scientific research?

Conditional probability is an essential tool in scientific research as it allows for the calculation of the likelihood of an event occurring given certain conditions. This is particularly useful in fields such as genetics, where researchers need to understand the likelihood of certain traits being passed down based on the presence or absence of certain genes.

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