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Pankaj K
Student’s Rating 4.97/5
I am an expert in Computational Statistics. Learn complex concepts in a 1:1 online tutoring session, or ask me to do your homework. I will ensure top grades in your exams and homework.
“15 years of experience. Top Tutor, Ph.D. in Statistics from a top university of India.”
Neeraj K
Student’s Rating 4.93/5
I have vast experience in providing Computational Statistics online tutoring and homework help. I am a perfectionist, and it will show up in your grades!
“23 years of experience. Masters in Statistics from ISI”
Jitendra S
Student’s Rating 4.92/5
I can explain complex concepts in easy to understand manner and solve problems fast. Top grades are assured in your Computational Statistics exams and homework.
“6 years of experience. Expert in Statistics and Probability.”
Student’s Reviews
Computational Statistics made simple
I have been struggling for the last 3 months in this course. After trying 4 tutors, I found MEB, and I got what I wanted. The tutor explained complex concepts in easy to digest manner. Many thanks!
Dana
University of Colorado
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Kuwait university
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AUM
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About Computational statistics
What is Computational statistics?
Computational statistics is the use of computers to do computationally intensive statistical analysis. It is also called Statistical Computing in everyday language and is a field that combines statistics with computer science. Some examples are:
 Markov chain Monte Carlo (MCMC) methods
 Local regression
 Newton Raphson
 Kernel density estimation
 Gradient Descent
 ExpectationMaximization
 ANN or Artificial neural networks
 Machine Learning
 Resampling methods
 Generalized additive models
What is the Monte Carlo Simulation/method?
Monte Carlo Simulation/method is a mathematical technique used in computational statistics, used to evaluate the outcomes of an uncertain event. Using algorithms, we do repeat random sampling to get the results. It is also known as multiple probability simulation.
Computational Statistics is used in biology, linguistics, physics, mathematics, materials science, etc.
Important topics in Computational Statistics
 Optimization And Solving Nonlinear Equations: Univariate and Multivariate Problems
 Combinatorial Optimization: NPCompleteness, Hard Problems, Genetic and Tabu Algorithms
 EM Optimization Methods: The EM Algorithm, EM Variants
 Numerical Integration: Romberg Integration, Gaussian Quadrature, Newton–Coˆtes Quadrature,
 Simulation And Monte Carlo Integration: Monte Carlo Method, Exact And Approximate Simulation, Techniques For Variance Reduction
 MCMC Or Markov Chain Monte Carlo: MetropolisHastings Algorithm, Gibbs Sampling, Implementation, Adaptive MCMC, Reversible Jump MCMC, Auxiliary Variable Methods, Markov Chain Maximum Likelihood
 Bootstrapping: Bootstrapping Principle, Inference, Performance and Uses, Monte Carlo Error Reduction,
 Nonparametric Density Estimation, Bivariate Smoothing, Multivariate Smoothing
(Topics credit: Computational Statistics, Second Edition by Geof H. Givens, Jennifer A. Hoeting)
Can I learn Computational statistics on my own, or do I need a tutor in Computational statistics?
A course in Computational statistics can be very complex due to the use of advanced mathematical concepts and tools. It also integrates computer science principles and algorithms. So a strong base in both these fields is a must to learn on your own. If you need help with your homework or want someone to explain the concepts in an online tutoring session, contact us 24×7 on WhatsApp. Our Computational statistics tutors are waiting to help you.
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