Nabin K. Malakar, Ph.D.

NASA JPL
I am a computational physicist working on societal applications of machine-learning techniques.

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My research interests span multi-disciplinary fields involving Societal applications of Machine Learning, Decision-theoretic approach to automated Experimental Design, Bayesian statistical data analysis and signal processing.

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Interested about the picture? Autonomous experimental design allows us to answer the question of where to take the measurements. More about it is here...

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I addition to the research, I also like to hike, bike, read and play with water color.

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Showing posts with label algorithm. Show all posts
Showing posts with label algorithm. Show all posts

Tuesday, April 2, 2013

#NASA's @AstroRobonaut Challenge

Here is the challenge:
Improve the vision algorithms of  @AstroRobonaut, and win some money. http://t.co/ccwbDOTUNi

A Robonaut is a dexterous humanoid robot built and designed at NASA Johnson Space Center in Houston, Texas. You can join him on facebook:
https://www.facebook.com/NASArobonaut
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Friday, June 18, 2010

Diffusive Nested Sampling: Brewer et. al.

Brendon et. al. has a newer version of nested sampling algorithm, they call it Diffusive Nested Sampling (DNS). As the name indicates, it principally differs from the "classic" nested sampling in presenting the hard constraint. It relaxes the hard evolving constraint and lets the samples to explore the mixture distribution of nested probability distributions, each successive distribution occupying e^-1 times the enclosed prior mass of the previously seen distributions. The mixture distribution is weighted at will (a hack :P) which is a clever trick of exploration. This reinforces the idea of "no peaks left behind" for multimodal problems.


On a test problem they claim that DNS "can achieve four times the accuracy of classic Nested Sampling, for the same computational effort; equivalent to a factor of 16 speedup".


I have not played with it yet. However, it seems worth trying. Just a note to myself.


PS:
What can grow out of side talks in a conference?
If you know the power of scrapping in the napkin paper, you would not be surprised.

The paper is available in arxiv:
http://arxiv.org/abs/0912.2380
The code is available at: http://lindor.physics.ucsb.edu/DNest/; comes with handy instructions.

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Thanks are due to Dr. Brewer for indicating typos in the draft and suggestions + allowing to use the figures.
 The original nested sampling code is available in the book by sivia and skilling: Data Analysis: A Bayesian Tutorial
Data Analysis: A Bayesian Tutorial 
Edit: Sep 5, 2013 An illustrative animation of Diffusive Nested Sampling (www.github.com/eggplantbren/DNest3) sampling a multimodal posterior distribution. The size of the yellow circle indicates the importance weight. The method can travel between the modes because the target distribution includes the (uniform) prior as a mixture component.