going Bayesian
Sep. 4th, 2007 05:06 pmI'm now trying to show that ICA is totally useless for the Gaussian case. The idea being that ICA does not test for conditional independence.
Apparently, Peter has been imagining otherwise, since in the Gaussian case, a regression coefficient being close to 0 implies conditional independence.
I really pushed for my idea of Bayesianizing ICA, in order to deal with the more general case in which the structure of the problem is a general multi-level DAG, instead of the Cocktail Party Problem (CPP). He finally agreed that I could be right. To make sure, I'm now going to evaluate ShimizuSearch against a new method I'll name StupidSearch, which just spits out a random graph (OR an empty graph OR a random full DAG).
However, I'm still intending to follow his suggestion of studying multiple-indicator graphs and whether it's possible to get a unique maximum-likelihood latent values for each data point; and to read economics literature that uses time series.
Some ideas of where to look for linear systems:
* electrical circuits (we could apply this to debugging of circuits)
* traffic flow at large temporal scale (e.g. > 1 day)
* water flow in rivers and streams
* economics (no reason to be linear, but no-one will complain, since they've always been making the linearity assumption)
Apparently, Peter has been imagining otherwise, since in the Gaussian case, a regression coefficient being close to 0 implies conditional independence.
I really pushed for my idea of Bayesianizing ICA, in order to deal with the more general case in which the structure of the problem is a general multi-level DAG, instead of the Cocktail Party Problem (CPP). He finally agreed that I could be right. To make sure, I'm now going to evaluate ShimizuSearch against a new method I'll name StupidSearch, which just spits out a random graph (OR an empty graph OR a random full DAG).
However, I'm still intending to follow his suggestion of studying multiple-indicator graphs and whether it's possible to get a unique maximum-likelihood latent values for each data point; and to read economics literature that uses time series.
Some ideas of where to look for linear systems:
* electrical circuits (we could apply this to debugging of circuits)
* traffic flow at large temporal scale (e.g. > 1 day)
* water flow in rivers and streams
* economics (no reason to be linear, but no-one will complain, since they've always been making the linearity assumption)
(no subject)
Date: 2007-09-05 01:09 am (UTC)ICA
Date: 2007-09-05 04:05 am (UTC)Re: ICA
Date: 2007-09-05 11:23 am (UTC)(no subject)
Date: 2007-09-05 04:07 am (UTC)(no subject)
Date: 2007-09-05 11:28 am (UTC)