I have uploaded some pictures in Facebook. The link is

http://www.facebook.com/

NASA JPL

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

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.

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

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

Welcome to nabinkm.com. Please visit again.

I will be visiting France for a week to attend the MaxEnt 2010 Conference.

There, I will be presenting my work on "Entropy Based Search Algorithm for Experimental Design".

http://maxent2010.inrialpes.fr/program/complete-program/#6.1

I have uploaded some pictures in Facebook. The link is

http://www.facebook.com/album.php?aid=179722&id=671171228&l=915fdf0f5a

I have uploaded some pictures in Facebook. The link is

http://www.facebook.com/

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".

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

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.
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.

---

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

This sounds fascinating concept; equally impressive to grasp!

In an article published in scientific american, Humans Carry More Bacterial Cells than Human Ones, scientists claim human body to contain more bacterial cell than the human cell itself. So if you have 100 trillion cells in your body, about the same number of bacteria are are paying you homage. Nice host. Moreover, it has also been reported that they have also contributed to human genes (http://en.wikipedia.org/wiki/Human_Genome_Project, http://www.ornl.gov/sci/techresources/Human_Genome/home.shtml). Strangely, other species seem to have less connections with bacteria; or may be it is yet to be discovered.

By definition, Ecosystem is a functional unit consisting of living things in a given area, non-living chemical and physical factors of their environment, linked together through nutrient cycle and energy flow. Since they help to maintain various body processes, this makes human as a host and the body as an ecosystem.

We had already learnt that some bacteria were friendly and some were not. Identification of pathogenic bacteria and use of antibiotic treatment has been hailed as one of the great success in medical history. The side effects of antibiotics are not so unfamiliar and reasoned as killing off pathogenic as well as friendly bacteria. However, once we are able to understand the ecosystem of human body, curing "infectious" diseases should be just a treat load of another identified bacteria! Shall we call it**Green Medicine**?

In an article published in scientific american, Humans Carry More Bacterial Cells than Human Ones, scientists claim human body to contain more bacterial cell than the human cell itself. So if you have 100 trillion cells in your body, about the same number of bacteria are are paying you homage. Nice host. Moreover, it has also been reported that they have also contributed to human genes (http://en.wikipedia.org/wiki/Human_Genome_Project, http://www.ornl.gov/sci/techresources/Human_Genome/home.shtml). Strangely, other species seem to have less connections with bacteria; or may be it is yet to be discovered.

By definition, Ecosystem is a functional unit consisting of living things in a given area, non-living chemical and physical factors of their environment, linked together through nutrient cycle and energy flow. Since they help to maintain various body processes, this makes human as a host and the body as an ecosystem.

We had already learnt that some bacteria were friendly and some were not. Identification of pathogenic bacteria and use of antibiotic treatment has been hailed as one of the great success in medical history. The side effects of antibiotics are not so unfamiliar and reasoned as killing off pathogenic as well as friendly bacteria. However, once we are able to understand the ecosystem of human body, curing "infectious" diseases should be just a treat load of another identified bacteria! Shall we call it

Nested Sampling was developed by John Skilling (http://www.inference.phy.cam.ac.uk/bayesys/box/nested.pdf // http://ba.stat.cmu.edu/journal/2006/vol01/issue04/skilling.pdf).

Nested Sampling is a modified Markov Chain Monte Carlo algorithm which can be used to explore the posterior probability for the given model. The power of Nested Sampling algorithm lies in the fact that it is designed to compute both the mean posterior probability as well as the Evidence. The algorithm is initialized by randomly taking samples from the prior. The algorithm contracts the distribution of samples around high likelihood regions by discarding the sample with the least likelihood, Lworst.

To keep the number of samples constant, another sample is chosen at random and duplicated. This sample is then randomized by taking Markov chain Monte Carlo steps subject to a hard constraint so that its move is accepted only if the new likelihood is greater than the new threshold, L > Lworst. This ensures that the distribution of samples remains uniformly distributed and that new samples have likelihoods greater than the current likelihood threshold. This process is iterated until the convergence. The logarithm of the evidence is given by the area of the sorted log likelihood as a function of prior mass. When the algorithm has converged one can compute the mean parameter values as well as the log evidence.

For a nice description of Nested Sampling, the book by Sivia and Skilling is highly recommended: Data Analysis: A Bayesian Tutorial.

The codes in C/python/R with an example of light house problem is available at:

http://www.inference.phy.cam.ac.uk/bayesys/

The paper is available at:

http://www.inference.phy.cam.ac.uk/bayesys/nest.ps.gz

Nested Sampling is a modified Markov Chain Monte Carlo algorithm which can be used to explore the posterior probability for the given model. The power of Nested Sampling algorithm lies in the fact that it is designed to compute both the mean posterior probability as well as the Evidence. The algorithm is initialized by randomly taking samples from the prior. The algorithm contracts the distribution of samples around high likelihood regions by discarding the sample with the least likelihood, Lworst.

To keep the number of samples constant, another sample is chosen at random and duplicated. This sample is then randomized by taking Markov chain Monte Carlo steps subject to a hard constraint so that its move is accepted only if the new likelihood is greater than the new threshold, L > Lworst. This ensures that the distribution of samples remains uniformly distributed and that new samples have likelihoods greater than the current likelihood threshold. This process is iterated until the convergence. The logarithm of the evidence is given by the area of the sorted log likelihood as a function of prior mass. When the algorithm has converged one can compute the mean parameter values as well as the log evidence.

For a nice description of Nested Sampling, the book by Sivia and Skilling is highly recommended: Data Analysis: A Bayesian Tutorial.

The codes in C/python/R with an example of light house problem is available at:

http://www.inference.phy.cam.ac.uk/bayesys/

The paper is available at:

http://www.inference.phy.cam.ac.uk/bayesys/nest.ps.gz

How do you learn?

As a student, I have always been inspired by the class environment for teaching and learning.

One of the best way that I could point out is the fact that students learn by the looking at what the teacher is doing to solve the problem. For example, when my teacher was teaching the anatomy of an earthworm, just by looking at the picture, the way he drew it, I mastered it as soon as he finished drawing. Segment by segment, organ by organ. That was one of the amazing experience of biology class with me. By drawing the figure along with hearing the description worked at that time. Similarly, I had a full body size human skeleton system drawn on my wall. It just worked straight out of board into brain.

So, when people talk about the interactive display of pictures in the biology classes, I feel what if I was in that class. What if my teacher had decided to bring a poster of earthworm instead of drawing it in the borad? could I learn it the same way?

Different students have different ways of learning. That was just one of the several case with me. Some people better learn by looking at the picture while being described. We all learn differently.

There are basic three kinds pointed in literatues:

Being innovative is rewarding because here is the tricky part:**they are going to teach your kids some day**.

:P

As a student, I have always been inspired by the class environment for teaching and learning.

One of the best way that I could point out is the fact that students learn by the looking at what the teacher is doing to solve the problem. For example, when my teacher was teaching the anatomy of an earthworm, just by looking at the picture, the way he drew it, I mastered it as soon as he finished drawing. Segment by segment, organ by organ. That was one of the amazing experience of biology class with me. By drawing the figure along with hearing the description worked at that time. Similarly, I had a full body size human skeleton system drawn on my wall. It just worked straight out of board into brain.

So, when people talk about the interactive display of pictures in the biology classes, I feel what if I was in that class. What if my teacher had decided to bring a poster of earthworm instead of drawing it in the borad? could I learn it the same way?

Different students have different ways of learning. That was just one of the several case with me. Some people better learn by looking at the picture while being described. We all learn differently.

There are basic three kinds pointed in literatues:

- kinesthetic
- visual
- auditory

Being innovative is rewarding because here is the tricky part:

:P

**Nabin K. Malakar, Ph.D. **

Worcester State University, MA

**Research Interests **

Societal applications of machine learning and remote sensing, Bayesian data analysis and signal processing, intelligent instruments, multisensor fusion etc.

Thank you for visiting.