So I got accepted to Google Inc Mountainview CA for a summer internship. Not only am I excited but I am also encouraged. My last claim to fame happened in Aug 2008. The long dry spell was killing me. Although, this means a lot of work it also means I am worth it :)
I was also thinking about the things I need to occupy my summer with and here they are
1. Google Internship: Do a good job at it
2. Develop C/C++ version of the Gibbs Sampler and look at implementation issues in a large-large database
3. Look into usability of MapReduce for Unigram/VSM/LDA-combined representation to come up with a really neat representation
4. Push out the IEEE-T-SE paper on comparitive study ... May 18
5. Push out the IEEE-T-KDE paper on Unigram + LDA ... May 18
6. Look at my Lemur-toolkit extensions and get back in touch with that code
7. Develop a GUI for retrieval engine and document existing matlab/c/c++ code
Cant wait for life to begin!!!
Showing posts with label Bayesian. Show all posts
Showing posts with label Bayesian. Show all posts
Thursday, April 29, 2010
Wednesday, March 25, 2009
Confusions with model parameter estimation and Sampling techniques
Lately, I have been digging in deep into Bayesian parameter estimation and how it works with the MCMC sampling techniques. Most of the tutorials leave a gap open in their efforts on explaining what role do MCMC techniques play in parameter estimation or filtering or prediction? Can the two be separated at all from a theoretical point at least? With the advent of recursive bayesian estimation the thin line that separates the two is getting smudged or erased.
Here I am trying to bring it all under one roof. The punch line is this.
"Bayesian Parameter estimation employs sampling and its only one of the steps in estimation.
When we are doing predictive distribution and filtering sampling becomes an important step in calculating the predicted and/or values and their corresponding distribution."
Some of the main techniques in parameter estimation are the EM, MAP, variational EM, stochastic EM and particle filters etc
Sampling techniques involve MCMC, Gibbs, MH, Importance sampling, sequential importance sampling.
Sample->estimate parameters->sample->estimate parameters
This cycle goes on till we have reached optimal parameter values
Here I am trying to bring it all under one roof. The punch line is this.
"Bayesian Parameter estimation employs sampling and its only one of the steps in estimation.
When we are doing predictive distribution and filtering sampling becomes an important step in calculating the predicted and/or values and their corresponding distribution."
Some of the main techniques in parameter estimation are the EM, MAP, variational EM, stochastic EM and particle filters etc
Sampling techniques involve MCMC, Gibbs, MH, Importance sampling, sequential importance sampling.
Sample->estimate parameters->sample->estimate parameters
This cycle goes on till we have reached optimal parameter values
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