jerad
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 Mar7 awarded Commentator Mar7 comment faked coin probability try reading this en.wikipedia.org/wiki/Geometric_distribution Dec17 comment Inference in a probabilistic Bayes network See this document here under the subsection "Known Structure, Partial Observability" Dec16 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? Dec15 comment Marginal pmf of Hierarchical model Is section 2.7 in this note of any use to you? Dec15 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? Dec15 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 $u$. Dec12 awarded Supporter Dec4 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. Dec3 comment Trying to understand the basics of bayesian inference The first equation is a distribution over $t$ conditional on some $x,w,\sigma^2$. Dec2 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.