Rich Ulrich <[EMAIL PROTECTED]> wrote: > On Mon, 20 Oct 2003 18:45:03 -0700, "albinali" <[EMAIL PROTECTED]> > wrote: > > > .... The data set set collected from the space has > > sensor readings along with the activities. Moreover, I want to > > construct a bayesian network for every activity. > > Is this something I should know, > 'What's a Bayesian network'?
Given a joint distribution over several variables, in principle one can always compute the marginal distribution over some of them, perhaps conditional on known values of some others, by integrating the joint distribution. A joint distribution specified as a product of conditional distributions can be given a pictorial form by drawing an oval for each variable and drawing arrows from the variables on the right side of the bar to the variables on the left side of the bar. E.g., p(A,B,C) = p(C|B) p(B|A) p(A) yields (A) -> (B) -> (C). There are other ways to decompose a joint distribution which lead to an undirected graph or a mixed directed-undirected graph. It turns out that conditional independence can be determined by studying the graph (ovals and arrows). This is important because conditional independence simplifies the calculations; a problem which is intractable may become tractable if we can find a simpler equivalent form. What is "Bayesian" about this scheme is that the variables A, B, C, what have you, can be any kind of quantities; experimental outcomes, parameters, hypotheses, etc. -- not restricted to "random variables". A Bayesian network is just a graphical representation of an ordinary Bayesian model. See, for example, http://auai.org/ and the links there. For what it's worth, Robert Dodier -- If I have not seen as far as others, it is because giants were standing on my shoulders. -- Hal Abelson . . ================================================================= Instructions for joining and leaving this list, remarks about the problem of INAPPROPRIATE MESSAGES, and archives are available at: . http://jse.stat.ncsu.edu/ . =================================================================
