Finite Difference Method : Solve Partial Differential Equations

KTU: ME305: COMPUTER PROGRAMMING & NUMERICAL METHODS |Module: VI: Solution of Partial Differential Equations: Classification, Finite Difference Method| Discussing numerical technique to solve a PDE. We approximate first and second order partial derivatives using finite differences. Read more...

Linear Correlation, Measures of Correlation

KTU: ME305: COMPUTER PROGRAMMING & NUMERICAL METHODS | Module VI: Curve fitting: Linear correlation, Measures of Correlation.| Correlation refers to the degree of relationship between two variables. Read more...

Curve Fitting – Method of Least Squares

KTU: ME305: COMPUTER PROGRAMMING & NUMERICAL METHODS | Module VI: Curve fitting: Method of Least Squares - Non-linear relationships, Linear correlation.| Curve Fitting is the process of establishing a mathematical relationship or a best fit curve to a given set of data points. Read more...

Aitken Technique for Interpolation

KTU: ME305: COMPUTER PROGRAMMING & NUMERICAL METHODS |Module: V: Interpolation.| Aitken technique can be easily implemented in a computer program and hence is a popular interpolation technique. Read more...

Errors and Approximations in Numerical Methods

KTU: ME305 : COMPUTER PROGRAMMING & NUMERICAL METHODS | Module: V: Errors and approximations, Sources of errors. | In a numerical process, errors can creep in from various sources. Read more to understand how various errors arise, progress and impact accuracy...

Solved Problems – Baye’s Theorem

Q1. There are three bags. First bag contains 1 white, 2 red and 3 green balls. Second bag contains 2 white, 3 red and 1 green balls. Third bag contains 3 white, 1 red and 2 green bals. A bag is chosen at random and 2 balls are drawn from it. These are found to be 1 white and 1 red. Find the probability that the balls so drawn came from the second bag. Solution: Let A1 be the event of choosing first bag Let A2 be the event of choosing second bag…continue reading →

Baye’s Theorem

Bayes theorem - Also known as Theorem of Inverse Probability Let A1,A2,...An be a set of exhaustive and mutually exclusive events of the sample space S with for each i. Let E be any event in S. Then Proof We have Therefore, Therefore,continue reading →
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