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Computing methods is an approach for constructing systems which are computationally intelligent, possess human like expertise in particular domain, can adapt to the changing environment and can learn to do better can explain their decisions. Computations must be completed within a reasonable time period.

The various type of Computation is the following :-

Message-Passing Computing is a method of creating separate processes for execution on different computers. It is a method of sending and receiving messages.

Pipelined Computation is a problem  divided  into  a  series  of  tasks  that  have  to  be  completed one after the other. Each task executed by a separate process or processor.

Ideal Parallel Computation is a computation that can obviously be divided into a number of completely individual parts. Each of which can be executed by a separate processor.

Hard computing is based on the concept of precise modeling and analyzing to yield accurate results. It works well for simple problems.

Soft computing aims to surmount NP-complete problems. It uses inexact methods to give useful but inexact answers to intractable problems. It represents a significant paradigm shift in the aims of computing - a shift which reflects the human mind.

The Computing methods provide an alternative for such complicated calculations. The following are the advantages in using computational methods.

They are extremely powerful problem solving tools. It is capable of handling large system of equations, non-linearities, complicated geometries.Computational methods reduce higher mathematics to basic arithmetic operations.The results can be viewed dynamically at the design stage and possible to control the errors due to various approximations.

Markov chains, Markov Chain Monte Carlo, Metropolis-Hasting, Gibbs sampler, MCMC in DNA motif discovery, MCMC in DNA motif discovery, Marginalization, General conditional sampling.

Some of the homework help topics include :

  • Convergence rate, Optimization, Newton-Raphson, Scoring, EM algorithm, Applications of EM, Intro to Linux, Intro to LATEX, Intro to R, R packages, Newton's Method, Simulation studies, Database concepts and Open Office Base
  • Reading data into SAS, File handling in SAS, arrays, Formats; reports; proc tabulate, Data checking and validation, SAS macro language, Parallel and grid computing, Setting up our statistical working environment, R, LaTeX, mySQL, Graphics
  • Random numbers generation ,Monte Carlo methods (variance reduction technique; computation of definite integrals) ,Optimizations,Numerical integrations,Data Basics,Exploratory data analysis / Graphics,Basics of R programming,T-tests and ANOVA

Importance sampling, Variance reduction, Gaussian quadrature, Romberg integration, Methods of data description, SAS:, descriptive statistics, graphical presentation, estimation, hypothesis testing, sample size, power, learning statistical methods, statistical computing ,Least squares, Penalized and weighted least squares, Density estimation and smoothing, Matrix computations, Optimization, Newton-Raphson, Fisher scoring.

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  • Convergence rate of EM, Model-based Clustering, Mixtures of Factor Analyzers, MM algorithm, Random number generation, random numbers generation ,Monte Carlo methods (variance reduction technique; computation of definite integrals) ,Optimizations,Numerical integrations,Data Basics,Exploratory data analysis / Graphics,Basics of R programming
  • T-tests and ANOVA,Regression and correlation,Tabular data and analysis of categorical data,Logistic Regression and Survival Analysis,R environment and R programming,Getting Started Using R,Using the R Help System,Data structure and data type, basic R programming and convention,Graphics in R,Random Variables and Probability,Discrete Distributions and Continuous Distributions,QQ Plot, PP Plot, and Comparison of Distributions in R
  • Generation of Random Numbers and Patterns,Simulation in R,Dataframe in R: import and export data, data manipulation,Basic statistical methods,Describing data in R,Functions and self-defined functions in R,General linear modeling and its implementation in R,Analysis of Variance in R,Bayesian Statistics in R
  • Combinatorial optimization, Integration, Quadrature, Laplace approximation, Resampling and Monte Carlo inferences, Jackknife and Bootstrap, Permutation procedures, Monte Carlo simulation, Statistical graphics ,nonlinear equations: bisection, functional iteration, Newton’s method.

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