Weaving together Astronomy+Statistics+Computer Science+Engineering+Intrumentation, far beyond the growing borders
Archive for the ‘Quotes’ Category.
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’ »
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’ »
What XKCD says:
The mouseover text on the original says “Correlation doesn’t imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there’.”
It is a bad habit, hard to break, the temptation is great.
I’ve been complaining about how one can do machine learning on solar images without a training set? (see my comment at the big picture). On the other hand, I’m also aware of challenges in astronomy that data (images) cannot be transformed freely and be fed into standard machine learning algorithms. Tailoring data pipelining, cleaning, and processing to currently existing vision algorithms may not be achievable. The hope of automatizing the detection/identification procedure of interesting features (e.g. flares and loops) and forecasting events on the surface of the Sun is only a dream. Even though the level of image data stream is that of tsunami, we might have to depend on human eyes to comb out interesting features on the Sun until the new paradigm of automatized feature identification algorithms based on a single image i.e. without a training set. The good news is that human eyes have done a superb job! Continue reading ‘An excerpt from …’ »
Student’s t-distribution is somewhat underrepresented in the astronomical community. Having an article with nice stories, it looks to me the best way to introduce the t distribution. This article describing historic anecdotes about monumental statistical developments occurred about 100 years ago.
Guinness, Gosset, Fisher, and Small Samples by Joan Fisher Box
Source: Statist. Sci. Volume 2, Number 1 (1987), 45-52.
No time for reading the whole article? I hope you have a few minutes to read following quotes, which are quite enchanting to me. Continue reading ‘Guinness, Gosset, Fisher, and Small Samples’ »
The first issue of this year’s IMS bulletin has an obituary, from which the following is quoted. Continue reading ‘Circumspect frequentist’ »
We have seen the word “bipartisan” often during the election and during the on-going recession period. Sometimes, I think that the bipartisanship is not driven by politicians but it’s driven by media, commentator, and interpreters. Continue reading ‘Bipartisanship’ »
Almost two year long scrutinizing some publications by astronomers gave me enough impression that astronomers live in the Gaussian world. You are likely to object this statement by saying that astronomers know and use Poisson, binomial, Pareto (power laws), Weibull, exponential, Laplace (Cauchy), Gamma, and some other distributions.[1] This is true. I witness that these distributions are referred in many publications; however, when it comes to obtaining “BEST FIT estimates for the parameters of interest” and “their ERROR (BARS)”, suddenly everything goes back to the Gaussian world.[2]
Borel Cantelli Lemma (from Planet Math): because of mathematical symbols, a link was made but any probability books have the lemma with proofs and descriptions.
Continue reading ‘Borel Cantelli Lemma for the Gaussian World’ »
There will be a special session at the 213th AAS meeting on meaning from surveys and population studies (SPS). Until then, it might be useful to pull out some interesting and relevant papers and questions/challenges as a preliminary to the meeting. I will not list astronomical catalogs and surveys only, which are literally countless these days but will bring out some if they change the way how science is performed with a description of the catalog (the best example would be SDSS, Sloan Digital Sky Survey, to my knowledge). Continue reading ‘[SPS] Testing Completeness’ »
Last month, Senator McCain (R-AZ) wildly dissed on Chicago’s Adler Planetarium, characterizing a funding request on its behalf as “planetariums and other foolishness.” Continue reading ‘“planetariums and other foolishness”’ »
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’ »
All of a sudden, partially owing to a thought provoking talk about visualization by Felice Frankel at IIC, I recollected a book, The Grammar of Graphics by Leland Wilkinson (2nd Ed. – I partially read the 1st ed. and felt little of use several years ago because there seemed no link for visualization of data from astronomy.) Continue reading ‘[Book] The Grammar of Graphics’ »
In order to understand a learning procedure statistically it is necessary to identify two important aspects: its structural model and its error model. The former is most important since it determines the function space of the approximator, thereby characterizing the class of functions or hypothesis that can be accurately approximated with it. The error model specifies the distribution of random departures of sampled data from the structural model.
The absolutely phenomenal webcomic XKCD hits a home run again, this time sketching out the spatial structure of the Universe all the way from here to The Edge .. in log scale. Continue reading ‘There and back again’ »
To my personal thoughts, the history of astronomy is more interesting than the history of statistics. This may change tomorrow. Harvard statistics department (chair Xiao-Li Meng) organizes a symposium titled
Quintessential Contributions:
Celebrating Major Birthdays of Statistical Ideas and Their Inventors
When: Saturday, September 27, 2008, 9:45 AM – 5:00 PM
Where: Radcliffe Gymnasium, 18 Mason Street, Cambridge, MA