Kushal Veer Singh
PhD student in JNU, India
CMSC 451 Selected Lecture Notes. [syllabus] | [lecture notes] | [HW1-6,Q1] | [HW7-10,Q2,F] | [project] [simulators/parsers] | [language definitions] | [automata definitions] | [computable definitions] Note that this file is plain text and .gif files.
The plain text spells out Greek letters because old browsers did not display them. Outer loop variable in nested R foreach loop. Publications - Achuthsankar S. Nair - Official Website. Daniel M. Romero. Daniel M. Romero. [R-pkgs] igraph 0.6 released. R - extract negative values from a column. Covariance and Correlation. Loading web-font TeX/Math/Italic \newcommand{\var}{\text{var}} \newcommand{\sd}{\text{sd}}\newcommand{\cov}{\text{cov}}\newcommand{\cor}{\text{cor}}\newcommand{\mse}{\text{mse}}\renewcommand{\P}{\mathbb{P}}\newcommand{\E}{\mathbb{E}} \newcommand{\R}{\mathbb{R}} \newcommand{\N}{\mathbb{N}}\newcommand{\bs}{\boldsymbol} Recall that by taking the expected value of various transformations of a random variable, we can measure many interesting characteristics of the distribution of the variable.
In this section, we will study an expected value that measures a special type of relationship between two real-valued variables. This relationship is very important both in probability and statistics. Basic Theory Definitions As usual, our starting point is a random experiment with probability measure \P on an underlying sample space. Calculus and Probability 3. 3.
Mean, Median, Variance and Standard Deviation Mean In the last section we saw that if saving and loan institutions are continuously failing at a rate of 5% per year, then the associated probability density function is f(x) = 0.05e 0.05x, Terminology - Differences between variable and variate. Using Netvizz & Gephi to Analyze a Facebook Network. This post was originally featured on published on May 6th, 2010.
Since the website will be relaunched and the post removed, I have relocated the tutorial to my personal page so that the Gephi community can continue to benefit from it. If a picture is worth a thousand words, then a graph must be worth a thousand spreadsheet rows, right? A Facebook network rendered in Gephi Okay, maybe not, but for practitioners and researchers alike, data visualization can reveal insights that aren’t always obvious from looking at the raw data, no matter how well organized it may be.
Psychological Statistics. Forrest Young's Notes Copyright © 1999 by Forrest W.
Young. Forrest Young and Warren Sarle gathered the following judgments of preference for a set of automobiles from the staff of a large local statistical software house in 1982. The data are judgments, on a scale of 0-10 of the preference the judge has for the automobile (0 = no preference; 10 = maximum preference). Altogether, there were 25 subjects and 17 Automobiles. Note that for preference data the variables correspond tot he judges and the observations to the Automobiles. The Var Window (in this case, subjects) and a portion of the data are shown below: Note that ViSta cannot perform a Principal Components Analysis when there are more columns (subjects) than rows (automobiles), so we have choosen 14 of the subjects: About half the programmers and all of the non-programmers, including the company President, two people working in marketing and the grandmother of one of the programmers.
STAT 505 - Applied Multivariate Statistical Analysis. Printer-friendly version Dispersion: Variance, Stanard Deviation A variance measures the degree of spread (dispersion) in a variable’s values.
Theoretically, a population variance is the average squared difference between a variable’s values and the mean for that variable. The population variance for variable x_j is. Untitled. Ordination in R This page covers the R functions to perform multivariate ordination, including some methods beyond the default biplots for displaying your results graphically.
Some of these methods will use functions in the vegan package, which you should load and install (see here if you havent loaded packages before). The following four major ordination methods are covered: 1. Install R package without root access(on Linux) R is a nice compannon for stataitical people.From time to time, I have to install various packages to accomplish some tasks.
At my desktop (linux server), I have the root access, the installation is pretty simple, R packages can be downloaded here [Date order] [Name ordered] Start R console and fire the installation command, sudo R >install.packages(“boa”) or you can download the package and install it using the command line sudo R CMD INSTALL boa_1.1.7-2.tar.gz. Getting a subset of a data structure. Problem You want to do get a subset of the elements of a vector, matrix, or data frame.
Solution To get a subset based on some conditional criterion, the subset() function or indexing using square brackets can be used. In the examples here, both ways are shown. SNAP: Papers. Tcomp5.html. Contents Context-Free Grammars We have seen that all regular languages, even those containing an infinite number of strings, can be defined using REs with a finite number of terms.
Non-regular languages can also be finitely defined, but not using REs. What is the difference between Unix and DOS. Unix usually refers to a family of operating systems that are similar in function or descendants of the original Unix operating system developed at AT&T Bell Laboratories. This includes a large portion of modern operating systems, including: All Linux distributions (Red Hat, SuSE, Fedora, Debian, etc.)BSDs (FreeBSD, NetBSD, OpenBSD, DragonflyBSD, Mac OS X) Solaris HP-UXAIX Unix-like systems are multi-user and multiprocessing operating systems; that is, more than one user can be using the machine at one time, and each user can run multiple processes, or programs. DOS v UNIX. This document aims to briefly outline the differences and similarities between UNIX and DOS (MS-DOS, DR-DOS etc.).
This is so that newcomers to UNIX can get started quicker if they already know a little about DOS. For more information on specific commands, consult the UNIX on-line manual pages. For further information about UNIX in general, read UNIXhelp for Users. Contents 1. Number of Users. Breathing, Breathing Exercises, Techniques and Breathing for Anxiety. Linux Command Reference. R - Assigning results of a for loop to an empty matrix. ArsDigita University Curriculum. About the Curriculum The curriculum was modeled on the undergraduate CS program at MIT. Several of the courses were straightforward adoptions of MIT courses. A few were specifically designed for the program, which was roughly in line with the ACM's 2001 Model Curricula for Computing. The process of accreditation (ACM or CSAB/ABET) would have been pursued if the program had continued. Courses. Algorithms. Untitled. Installation - How to install a .tar.gz (or .tar.bz2) file.
Rmr2/docs/tutorial.md at master · RevolutionAnalytics/rmr2. What Is a Virtual Machine? [MakeUseOf Explains] Virtual machines allow you to run other operating systems within your current operating system – the operating systems will run as if they’re just another program on your computer. Virtual machines are ideal for testing out other operating systems – like the new Windows 8 or alternative Linux operating systems. You can also use virtual machines to run software on operating systems it wasn’t designed for – for example, you can run Windows programs on a Mac with a virtual machine. Do you want to get started with virtual machines?
You don’t have to pay anything – there are several great, free virtual machine programs. Parallel Programming Techniques. These notes are drawn heavily from Parallel Programming: Techniques and Applications Using Networked Workstations and Parallel Computers, Barry Wilkinson, Michael Allen, Prentice Hall, 1999. Talk like a native speaker - GONNA, HAVETA, WANNA. English Pronunciation - ABCDEFG - How to say letters! Roberto Todeschini. Lesson 5 - Functions. Introduction. Machine learning. Theory of Finite Automata. Undergraduate course in finite automata theory with introduction to formal languages. CS 43001 Compiler Construction Course. (Autumn Semester 2005) Theory Niloy Ganguly niloy@cse.iitkgp.ernet.in Laboratory Chitta Ranjan Mandal chitta@cse.iitkgp.ernet.inNiloy Ganguly niloy@cse.iitkgp.ernet.in. A Gentle Introduction to ML. How to draw two graphs in one scatterplot? Adding the Python Directory to the path Variable.
R: Package Index. Graph Theory: Measures and Indices. Several measures and indices can be used to analyze the network efficiency. How to calculate centrality measures for a 4 million edge network? R Resources - Daizaburo Shizuka. 3 expressions to improve your conversation skills. Human Language Sentences - Basic Parse Trees, X-Bar Theory & Ambiguity. Course Contents. MapReduce Basics. Slides from today’s Big Data Step-by-Step Tutorials: Infrastructure series and Intro to R+Hadoop with RHadoop’s rmr. VU Lectures.
Mark Newman: Publications. Ent.