Suppose we want to cluster a data stream of unknown number of clusters, and estimate them using particle filters. With particle filters, we need to know P(Xt  Xt−1) and P(Zt  Xt) (where z refer to the data (reports), and x refer to the estimated states (hypothesis)). Then my question is: how can we define P(Xt  Xt−1) (transition probability) and P(Zt  Xt) (observation/likelihood probability) in this context ? asked 21 May '12, 11:36 ShN
EllenL, it's strange, I don't see how to create a new question on the forum you provided (reddit.com) !
Even stranger is that I don't see that comment anywhere here, although an email arrived saying you posted it. And on reddit.com/r/aiclass your question is #1 but it's just a link back to here. Most mysterious.
(30 May '12, 15:52)
EllenL
IIRC reddit is different from Aiqus in that the headline is an external link and the reddit comment thread for the link is accessed by clicking on comment just below the headline.
(30 May '12, 23:47)
rseiter ♦
@EllenL I deleted the comment after I understood that reddit was just a list of links to other forums that you can post, it's not a forum.
(31 May '12, 12:06)
ShN

Access to the paper does not require IEEE membership or a subscription  see comment below. Here's the abstract (reformatted for readability):
answered 21 May '12, 20:49 EllenL I think you can also download it without subscription from: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.61.2931 (see link "cached" at the right) or directly from http://www.foi.se/fusion/fusion44.pdf
I don't understand what is the probability of false detection and the probability of probability of an unobserved target, on which the update model ( i.e. P(Xt  Xt−1) ) is defined.
It seems that the likelihood ( i.e. P(Zt  Xt) ) is defined as a Gaussian based on the std deviation and the mean distance of datapoints to their cluster representative. But I'm not sure, and it's not clear.
(22 May '12, 04:56)
ShN
Got it, thanks. Reading it will take a bit longer, though....
(22 May '12, 16:19)
EllenL
Ok, I am waiting for your answer then, thanks.
(28 May '12, 16:55)
ShN
I hope you're not expecting an expert opinion, because I'm just another student here. But I've printed out the article and I'm working my way through it bit by bit, looking for answers to your questions.
Perhaps rephrasing the question or giving more details would attract more attention. (That's what I tried to do when I posted the abstract, but no luck so far.) Back when the 2011 AI class was covering particle filters, this question would have found a better audience. Have you tried posting it in another forum, such as http://www.reddit.com/r/aiclass/?
(28 May '12, 18:08)
EllenL
1
Speaking of particle filters and the AI class, one of the Berkeley PacMan PAs covered particle filters (the Stanford version left this out) if you are interested in playing with it:
http://wwwinst.eecs.berkeley.edu/~cs188/pacman/projects/tracking/busters.html
(29 May '12, 00:10)
rseiter ♦
