Archive for the ‘Algorithms’ Category.

May 7th, 2009| 11:14 am | Posted by hlee

One of [ArXiv] papers from yesterday whose title might drag lots of attentions from astronomers. Furthermore, it’s a short paper.

[arxiv:math.CO:0905.0483] by Harmany, Marcia, and Willet.

Continue reading ‘[ArXiv] Sparse Poisson Intensity Reconstruction Algorithms’ »

Tags:

compressed sensing,

decomposition,

EM algorithm,

intensity,

MPLE,

multiscale,

penalty,

Poisson,

Poisson Intensity,

Sparcity,

wavelet Category:

Algorithms,

arXiv,

Astro,

Cross-Cultural,

Data Processing,

High-Energy,

Imaging,

Jargon |

Comment
Apr 16th, 2009| 07:32 am | Posted by vlk

http://www.tricki.org/

The wikipedia-like repository for mathematical “tricks” has now gone live. Their mission statement:

The main body of the Tricki will be a (large, if all goes according to plan) collection of articles about methods for solving mathematical problems. These will be everything from very general problem-solving tips such as, “If you can’t solve the problem, then try to invent an easier problem that sheds light on it,” to much more specific advice such as, “If you want to solve a linear differential equation, you can convert it into a polynomial equation by taking the Fourier transform.”

Tags:

Announcement,

math,

mathematics,

maths,

tricki,

tricks,

website,

Wikipedia Category:

Algorithms,

Cross-Cultural,

Jargon,

News,

Stat |

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Apr 2nd, 2009| 12:00 pm | Posted by hlee

I cannot remember when I first met **Chernoff face** but it hooked me up instantly. I always hoped for confronting multivariate data from astronomy applicable to this charming EDA method. Then, somewhat such eager faded, without realizing what’s happening. Tragically, this was mainly due to my absent mind. Continue reading ‘[MADS] Chernoff face’ »

Tags:

calibration,

Capella,

Chandra,

Chernoff face,

EDA,

line ratios,

MADS,

XAtlas Category:

Algorithms,

arXiv,

Astro,

Cross-Cultural,

Data Processing,

Jargon,

Methods,

Misc,

News,

Quotes,

Spectral,

Stars,

X-ray |

2 Comments
Mar 11th, 2009| 01:04 pm | Posted by hlee

by **T. Cover and J. Thomas** website: http://www.elementsofinformationtheory.com/

Once, perhaps more, I mentioned this book in my post with the most celebrated paper by Shannon (see the posting). Some additional recommendation of the book has been made to answer offline inquiries. And this book always has been in my favorite book list that I like to use for teaching. So, I’m not shy with recommending this book to astronomers with modern objective perspectives and practicality. Before advancing for more praises, I must say that those admiring words do not imply that I understand every line and problem of the book. Like many fields, Information theory has grown fast since the monumental debut paper by Shannon (1948) like the speed of astronomers observation techniques. Without the contents of this book, most of which came after Shannon (1948), internet, wireless communication, compression, etc could not have been conceived. Since the notion of “**entropy**“, the core of **information theory**, is familiar to astronomers (physicists), the book would be received better among them than statisticians. This book should be read easier to astronomers than statisticians. Continue reading ‘[Book] Elements of Information Theory’ »

Tags:

bandwidth,

book,

Cover,

data mining,

education,

Entropy,

Information theory,

Kolmogorov complexity,

Shannon,

Thomas Category:

Algorithms,

arXiv,

Cross-Cultural,

Data Processing,

Jargon,

Quotes |

Comment
Mar 6th, 2009| 03:42 pm | Posted by hlee

Ah ha~ Once I questioned, “what is systematic error?” (see [Q] systematic error.) Thanks to L. Lyons’ work discussed in [ArXiv] Particle Physics, I found this paper, titled **Systematic Errors** describing the concept and statistical inference related to **systematic errors** in the field of particle physics. It, gladly, shares lots of similarity with high energy astrophysics. Continue reading ‘systematic errors’ »

Tags:

coverage,

Heinrich,

likelihood,

Lyons,

nuisance parameter,

objective priors,

p-value,

particle physics,

statistical error,

subjective priors,

systematic error Category:

Algorithms,

arXiv,

Bayesian,

Cross-Cultural,

Data Processing,

Frequentist,

Jargon,

Misc,

News,

Physics,

Stat,

Uncertainty |

Comment
Jan 20th, 2009| 01:59 pm | Posted by hlee

Someone emailed me for globular cluster data sets I used in a proceeding paper, which was about how to determine the multi-modality (multiple populations) based on well known and new information criteria without binning the luminosity functions. I spent quite time to understand the data sets with suspicious numbers of globular cluster populations. On the other hand, obtaining globular cluster data sets was easy because of available data archives such as VizieR. Most data sets in charts/tables, I acquire those data from VizieR. In order to understand science behind those data sets, I check ADS. Well, actually it happens the other way around: check scientific background first to assess whether there is room for statistics, then search for available data sets. Continue reading ‘accessing data, easier than before but…’ »

Tags:

archive,

ascii,

catalog,

CDA,

data analysis,

data mining,

database,

Gator,

globular cluster,

inference,

massive data,

multimodality,

multiple populations,

NED,

SDSS,

statistical inference,

statistician,

streaming data,

table,

tabulated,

visieR Category:

Algorithms,

Astro,

Cross-Cultural,

Data Processing,

Jargon,

Meta,

Nuggets,

Objects |

3 Comments
Jan 15th, 2009| 06:01 pm | Posted by hlee

I wonder what Fisher, Neyman, and Pearson would say if they see “Technique” after “Likelihood Ratio” instead of “Test.” A presenter’s saying “Likelihood Ratio Technique” for source identification, I couldn’t resist checking it out not to offend founding fathers of the likelihood principle in statistics since “Technique” sounded derogatory to be attached with “Likelihood” to my ears. I thank, above all, the speaker who kindly gave me the reference about this likelihood ratio technique. Continue reading ‘Likelihood Ratio Technique’ »

Tags:

Fisher,

likelihood principle,

likelihood ratio technique,

likelihood ratio test,

Neyman,

Pearson Category:

Algorithms,

arXiv,

Astro,

Bayesian,

Cross-Cultural,

Data Processing,

Fitting,

Frequentist,

Jargon,

Methods,

Objects,

Stat,

Uncertainty |

Comment
Dec 11th, 2008| 03:46 pm | Posted by hlee

A few scientists in our group work on estimating the intensities of gamma ray observations from sky surveys. This work distinguishes from typical image processing which mostly concerns the point estimation of intensity at each pixel location and the size of overall white noise type error. Often times you will notice from image processing that the orthogonality between errors and sources, and the white noise assumptions. These assumptions are typical features in image processing utilities and modules. On the other hand, CHASC scientists relate more general and broad statistical inference problems in estimating the intensity map, like intensity uncertainties at each point and the scientifically informative display of the intensity map with uncertainty according to the Poisson count model and constraints from physics and the instrument, where the field, **multiscale modeling** is associated. Continue reading ‘[MADS] multiscale modeling’ »

Nov 17th, 2008| 01:39 pm | Posted by hlee

The full description is given http://cxc.harvard.edu/ciao3.4/ahelp/bayes.html about “bayes” under sherpa/ciao^{[1]}. Some sentences kept bothering me and here’s my account for the reason given outside of quotes. Continue reading ‘It bothers me.’ »

Tags:

bayes,

ciao,

ML,

Sherpa Category:

Algorithms,

Astro,

Cross-Cultural,

Data Processing,

Fitting,

High-Energy,

Jargon,

Languages,

Methods,

Spectral,

Uncertainty,

X-ray |

4 Comments
Oct 23rd, 2008| 12:13 pm | Posted by hlee

I’ve talked about IMSL on my pyIMSL post, which is a commercial scientific library. There is a GNU version of IMSL, **GSL.** Finding GSL is the courtesy of Jiangang, who was the author of the poster that I most liked from the 212th AAS, (see *My first AAS. V. measurement error and EM* and his comment.) Continue reading ‘GSL – GNU Scientific Library’ »

Oct 9th, 2008| 04:28 pm | Posted by hlee

Without signal processing courses, the following equation should be awfully familiar to astronomers of photometry and handling data:

$$c_k=\int_\Lambda l(\lambda) r(\lambda) f_k(\lambda) \alpha(\lambda) d\lambda +n_k$$

Terms are in order, camera response (c_k), light source (l), spectral radiance by l (r), filter (f), sensitivity (α), and noise (n_k), where Λ indicates the range of the spectrum in which the camera is sensitive.

Or simplified to $$c_k=\int_\Lambda \phi_k (\lambda) r(\lambda) d\lambda +n_k$$

where φ denotes the combined illuminant and the spectral sensitivity of the k-th channel, which goes by augmented spectral sensitivity. Well, we can skip spectral radiance r, though. Unfortunately, the sensitivity α has multiple layers, not a simple closed function of λ in astronomical photometry.

Or $$c_k=\Theta r +n$$

Inverting Θ and finding a reconstruction operator such that r=inv(Θ)c_k leads spectral reconstruction although Θ is, in general, not a square matrix. Otherwise, approach from indirect reconstruction. Continue reading ‘[tutorial] multispectral imaging, a case study’ »

Tags:

matrix,

Mona Lisa,

multispectral,

noise,

signal processing,

signal processing magazine,

Tutorial Category:

Algorithms,

arXiv,

Cross-Cultural,

Data Processing,

Fitting,

Imaging,

Methods,

Quotes,

Spectral,

Stat,

Uncertainty |

2 Comments
Sep 30th, 2008| 01:45 am | Posted by hlee

At least two images for reconstructing a 3D scene is a conventional belief. Yet, we do know that our eyes reconstruct 3D scenes from various single snap shot images, just with one picture. Based on our perception and learning ability or our internal pattern recognition ability, a few groups of people have been trying to reconstruct a 3D image from one still image picture. Luckily you can test such progress, reconstructing a 3D scene from a single still image at Make3D (a click brings you to Make3D at Stanford). Continue reading ‘Make3D’ »

Sep 18th, 2008| 07:48 pm | Posted by hlee

Another deduced conclusion from reading preprints listed in arxiv/astro-ph is that astronomers tend to confuse **classification and clustering** and to mix up methodologies. They tend to think any algorithms from classification or clustering analysis serve their purpose since both analysis algorithms, no matter what, look like a **black box**. I mean a black box as in neural network, which is one of classification algorithms. Continue reading ‘Classification and Clustering’ »

Tags:

black box,

book,

catalog,

Classification,

clustering,

haste,

outliers,

R,

Robert Serfling,

semi-supervised learning,

survey Category:

Algorithms,

arXiv,

Astro,

Bad AstroStat,

Cross-Cultural,

Data Processing,

Frequentist,

Jargon,

Methods,

Stat |

Comment
Sep 17th, 2008| 02:11 pm | Posted by hlee

I’ve been joking about the astronomers’ fashion in writing **Markov chain Monte Carlo (MCMC)**. Frequently, **MCMC** was represented by **Monte Carlo Markov Chain** in astronomical journals. I was curious about the history of this new creation. Overall, I thought it would be worth to learn more about the history of MCMC and this paper was up in arxiv: Continue reading ‘A History of Markov Chain Monte Carlo’ »

Tags:

BUGS,

data augmentation,

EM,

Gibbs sampling,

Hasting,

history,

Metropolis,

reversible jump,

simulated annealing Category:

Algorithms,

arXiv,

Bad AstroStat,

Bayesian,

Cross-Cultural,

Data Processing,

Imaging,

MC,

MCMC,

Methods,

Quotes,

Stat |

2 Comments
Sep 16th, 2008| 04:34 pm | Posted by hlee

Astronomers tend to think in Bayesian way, but their Bayesian implementation is very limited. OpenBUGS, WinBUGS, GeoBUGS (BUGS for geostatistics; for example, modeling spatial distribution), R2WinBUGS (R BUGS wrapper) or PyBUGS (Python BUGS wrapper) could boost their Bayesian eagerness. Oh, by the way, **BUGS** stands for **Bayesian inference Using Gibbs Sampling.** Continue reading ‘BUGS’ »

Tags:

openBUGS,

PyBUGS,

Python,

R,

toolbox,

winBUGS Category:

Algorithms,

Bayesian,

Data Processing,

Languages,

MCMC,

Methods,

News |

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