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Mar
7
awarded  Commentator
Mar
7
comment faked coin probability
try reading this en.wikipedia.org/wiki/Geometric_distribution
Dec
17
comment Inference in a probabilistic Bayes network
See this document here under the subsection "Known Structure, Partial Observability"
Dec
16
comment Inference in a probabilistic Bayes network
I what is the functional form of the conditional distribution $p(u|c)$? Is $u$ a discrete variable? Gaussian? What about c?
Dec
15
comment Marginal pmf of Hierarchical model
Is section 2.7 in this note of any use to you?
Dec
15
comment Inference in a probabilistic Bayes network
Are you assuming a particular type of distribution for $u and c$ or is this simply a general exercise?
Dec
15
comment Inference in a probabilistic Bayes network
@Kits89, I think tskuzzy's last statement just meant that according to your graphical model, if $c$ is observed then $w and q$ are independent of $u$.
Dec
12
awarded  Supporter
Dec
4
comment Trying to understand the basics of bayesian inference
Yes, well as explained in the wikipedia article on likelihood functions, it is merely a matter of perspective. I think anytime you see $p(\cdot)$ you should try to visualize a plot with probability on the Y-axis and parameters on the X-axis. You can either evaluate that function for a parameter value and return a probability, or you can view it as a function of the variables, ie. the whole plot.
Dec
3
comment Trying to understand the basics of bayesian inference
The first equation is a distribution over $t$ conditional on some $x,w,\sigma^2$.
Dec
2
comment Trying to understand the basics of bayesian inference
To answer your second question, the prior has $w$ as a variable because it is function of $w$. It maps all possible values of $w$ to a probability. Furthermore, it is a Gaussian. See footnote #3 in that paper...I think you're getting confused by the subtle distinction b/t a probability density and a likelihood function.