computation

Problem 1 Part (a) Part (b) Part (c) Interpretation Part (d) Part (e) Part (f) Part (g) Part (h) Problem 2 Part (a) Part (b) Part (c) Part (d) Interpretation Part (e) Appendix: code mixed.

In [1], the authors present a method for constructing a symbolic (nominal) representation for real-valued time series data. A symbolic representation is desirable because then it becomes possible to use many of the effective algorithms that require symbolic representation, like hashing and Markov models.

Problem 1 Solution: Newton’s method Solution: Secant method Comparison of Newton’s method versus secant method Problem 2 Solution Problem 3 Solution: part (a) Solution: part (b) Problem 4 Solution Problem 5 Solution Problem 6 Solution Problem 1 Write your own code and find solution to the equation \(x^3 + x - 4 = 0\) using Newton’s method and the secant method.

Problem 1 Part (a) Part (b) Part (c) E-step M-step Part (d) Problem 2 Part (a) E-step M-step Part (b) Part (c) Problem 1 Derive the E-M algorithm for right-censored normal data with known variance, say \(2 = 1\).

Problem 1 Part (a) Acceptance-rejection sampler Metropolis-Hastings algorithm Part (b) Part (c) Problem 2 (Problem 7.1) Part (a) Part (b) Numerical imprecision Part (c) Part (d) Part (e) Problem 3 Part (a) Part (b) Distribution of \(\mu|(\tau,\vec{x})\) Distribution of \(\tau|(\mu,\vec{x})\) Part (c) Additional analysis Problem 1 Use Metropolis Hasting algorithm to generate \(Y \sim \operatorname{GAM}(\alpha, 1)\), where \(\alpha > 1\).

Problem 1 Part (a) Part (b) Part (c) Interpretation Part (d) Comparing machines Comparing machine \(\times\) operator Part (e) Problem 2 Part (a) Part (b) Part (c) Interpretation Part (d) Part (e) Part (f) Appendix: Code Problem 1 The surface finish of metal parts made on \(a=4\) machines is under investigation.

Class of homomorphic monads that extend algebraic data types in some well-defined way for faciliating faster or more accurate calculations, such as the logarithm trick.