Advanced Bayesian Computation Assignment Help

The popularity of Bayesian statistics can be attributed in large part to advances in computing and developments in computational methods. There are currently two types of Bayesian computational methods. The first type uses iterative Monte Carlo simulation and includes the Gibbs sampler, the Metropolis-Hastings algorithm, Hamiltonian sampling, and other algorithms. These first-type methods typically generate a Markov chain with the target distribution as its stationary distribution. The second type of approximation is distributional, and it includes Laplace approximation (Laplace 1785, 1810), variational Bayes (Jordan et al. 1999), and so on. These methods of the second type attempt to find the distribution with the best analytical form that best approximates the target distribution. Sect. 3.1 discusses Markov chain Monte Carlo (MCMC) methods such as the general Metropolis-Hastings algorithm (M-H), Gibbs sampler with conjugacy, and Hamiltonian Monte Carlo (HMC). Section 3.2 discusses the convergence and efficiency of the sampling methods described above. In Section 3.3, we show how to specify a Bayesian model and draw model inferences using OpenBUGS and Stan.

KEY FEATURE

The popularity of Bayesian statistics can be attributed in large part to advances in computing and developments in computational methods.

The first type uses iterative Monte Carlo simulation and includes the Gibbs sampler, the Metropolis-Hastings algorithm, Hamiltonian sampling, and other algorithms

The second type of approximation is distributional, and it includes Laplace approximation (Laplace 1785, 1810), variational Bayes (Jordan et al. 1999), and so on.

These methods of the second type attempt to find the distribution with the best analytical form that best approximates the target distribution.

Advanced Bayesian Computation Assignment Help

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Key Topics

    What All You Can Learn From This Course:

    • Show how to create and implement Bayesian linear regression models in Python
    • List the most prominent hierarchical linear models in terms of regression coefficients.
    • Explain the concept of probability models and demonstrate the use of Bayesian methods for missing data problems
    • Demonstrate how to use Python to create probability models
    • Explain non-linear and non-parametric models in terms of coefficient shrinkage and multivariate regression.
    • Define the basic concepts of Gaussian process models
    • Recognise mixture model approaches for classification and regression
    • Define and list the essential Dirichlet process model properties
    • Demonstrate how to use PyMC3 to implement Bayesian inference models in Python
    • Recall hierarchical linear models from the perspective of regression coefficients, describe how to work with generalised linear models, and use PyMC3 to implement Bayesian inference

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