Integrals of real-valued functions. Numerical integration methods. B1.2b Linear ordinary differential Compute explicit solutions for linear Differential equations, general solution ordinary differential equations and apply equations with hand side. numerical methods to approximate Nonlinear differential solutions to non-linear differential equations, and linearization. equations. (I) Numerical methods for ordinary differential equations. B1.2c Iterative methods. numerical methods to find Numerical Rounding and numerical approximate solutions for non-linear solutions of errors. equations. non-linear equation (B) B1.3 Probability and statistics B1.3a Probability measures, density Define probability measures, derive Probabilities and functions associated formulae and calculate values Bayesian Mathematical expectation, from data. (B) estimation variance Covariance, correlation Select a distribution for a given random Conditional probabilities, variable and apply a Bayesian estimation (B, I) Bayes law method. (I) Minimum mean square estimation Distributions including normal, chi-squared, t and F
Integrals of real-valued functions. Numerical integration methods. B1.2b Differential equations (I) Linear ordinary differential equations, general solution with hand side. Nonlinear differential equations, and linearization. Numerical methods for ordinary differential equations. Compute explicit solutions for linear ordinary differential equations and apply numerical methods to approximate solutions to non-linear differential equations. B1.2c Numerical solutions of non-linear equation (B) Iterative methods. Rounding and numerical errors. numerical methods to find approximate solutions for non-linear equations. B1.3 Probability and statistics B1.3a Probabilities and Bayesian estimation (B, I) Probability measures, density functions Mathematical expectation, variance Covariance, correlation Conditional probabilities, Bayes law Minimum mean square estimation Distributions including normal, chi-squared, t and F Define probability measures, derive associated formulae and calculate values from data. (B) Select a distribution for a given random variable and apply a Bayesian estimation method. (I)
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