tag:blogger.com,1999:blog-19803222.post359069504429674264..comments2024-03-18T01:45:45.724-06:00Comments on natural language processing blog: Non-parametric versus model selection/averaginghalhttp://www.blogger.com/profile/02162908373916390369noreply@blogger.comBlogger3125tag:blogger.com,1999:blog-19803222.post-83814615179638026212009-05-12T10:40:00.000-06:002009-05-12T10:40:00.000-06:00酒店經紀PRETTY GIRL 台北酒店經紀人 ,禮服店 酒店兼差PRETTY GIRL酒店公關 酒...酒店經紀PRETTY GIRL <A HREF="http://www.taipeilady.com/" REL="nofollow" TITLE="台北酒店經紀人">台北酒店經紀人</A> ,<A HREF="http://tw.myblog.yahoo.com/jw!qZ9n..6QEhhc0LkItOBm/" REL="nofollow" TITLE="禮服店">禮服店</A> 酒店兼差PRETTY GIRL<A HREF="http://www.mashow.org/" REL="nofollow" TITLE="酒店公關">酒店公關</A> 酒店小姐 彩色爆米花<A HREF="http://blog.xuite.net/jkl338801/blog/" REL="nofollow" TITLE="酒店兼職">酒店兼職</A>,酒店工作 彩色爆米花<A HREF="http://tw.myblog.yahoo.com/jw!BIBoU5SeBRs21nb_ajFpncbTqXds" REL="nofollow" TITLE="酒店經紀">酒店經紀</A>, <A HREF="http://mypaper.pchome.com.tw/news/thomsan/3/1310065116/20080905040949/" REL="nofollow" TITLE="酒店上班">酒店上班</A>,酒店工作 PRETTY GIRL<A HREF="http://tw.myblog.yahoo.com/jw!rybqykeeER6TH3AKz1HQ5grm/" REL="nofollow" TITLE="酒店喝酒">酒店喝酒</A>酒店上班 彩色爆米花<A HREF="http://mypaper.pchome.com.tw/news/jkl338801/" REL="nofollow" TITLE="台北酒店">台北酒店</A>酒店小姐 PRETTY GIRL<A HREF="http://www.mashow.org/" REL="nofollow" TITLE="酒店上班">酒店上班</A>酒店打工PRETTY GIRL<A HREF="http://www.tpangel.com/" REL="nofollow" TITLE="酒店打工">酒店打工</A>酒店經紀 彩色爆米花Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-19803222.post-52450104468172842662007-10-25T21:07:00.000-06:002007-10-25T21:07:00.000-06:00i agree with mark on both counts. it's true that ...i agree with mark on both counts. <BR/><BR/>it's true that nonparametric bayesian models can often be approximated with finite parametric models. the Dirichlet process allows two approximations---one through a finite stick-breaking model and another through a symmetric Dirichlet.<BR/><BR/>still, even if the finite approximation of the nonparametric model does the trick, it's nice to know what is being approximated. (and this is particularly useful for setting and reasoning about hyperparameters.)<BR/><BR/>mark's second point is compelling. the promise of NPB models is in moving beyond simply choosing a number of components. NPB models that generate structures, like grammars or trees, allow us to posit complicated combinatorial objects as latent random variables and still hope to infer them from data.<BR/><BR/>the naysayer might say: this is simply search with an objective function that is the posterior. yes, this is true, at least when you only care about a MAP estimate. but, the NPB posterior gives a nicely regularized objective function trading off what the data imply and a prior preference for simpler (or more complicated) structures.david bleihttps://www.blogger.com/profile/06292909346075142113noreply@blogger.comtag:blogger.com,1999:blog-19803222.post-46745256650516776262007-10-25T18:36:00.000-06:002007-10-25T18:36:00.000-06:00This is an interesting issue! In fact, a Bayesian...This is an interesting issue! In fact, a Bayesian estimator for a parametric model may select a model that only uses a subset of the possible states, particularly if you have a sparse Bayesian prior. Indeed, one way of estimating a non-parametric model is to fit a corresponding parametric model with a state space sufficiently large that not all states will be occupied (or only occupied with very low probability).<BR/><BR/>I think this makes it clear that non-parametric models aren't necessarily that different to parametric ones.<BR/><BR/>The great hope (and at this stage I think that's all it is) for non-parametric models is that it will let us formulate and explore models of greater complexity than we could deal with parametrically.<BR/><BR/>If you'll excuse me patting my own back, I think that the adaptor grammars we presented at NIPS last year are an example of something that would be hard to formulate parametrically. At a very high level, adaptor grammars are an extension of PCFGs that permit an infinite number of possible rules. The possible rules are combinations of other useful rules, and so on recursively. So adaptor grammars are a single framework that integrates the two phases of standard generate-and-prune grammar learning systems (in which a rule-proposal phase is followed by a rule-probability estimation phase that prunes the useless rules).Mark Johnsonhttps://www.blogger.com/profile/05951121491616376798noreply@blogger.com