Applied Bayesian Statistics Assignment and Homework Help

Bayesian Statistics is a subset of the field of statistics in which the evidence about the true state of the world is expressed in terms of degrees of Bayesian probabilities. Bayesian analysis requires evaluating expectations of functions of random quantities as a basis for inference, where these quantities may have posterior distributions which are multivariate or of complex form or often both. Bayesian statistics is a system for describing epistemological uncertainty using the mathematical language of probability. Bayesian statistical methods start with existing 'prior' beliefs, and update these using data to give 'posterior' beliefs, which may be used as the basis for inferential decisions.


Students studying Bayesian Statistics can avail our help in completing their projects or assignments at a reasonable & minimal cost with quality par excellence in the following topics:

  • Applied Bayesian Statisticsive probability
  • Bayesian inference generalized linear model probability
  • Bayesian methodology with emphasis on applied statistical problem
  • Categorical data in Bayesian analysis
  • Change-of-variable formula
  • Common prior distributions
  • Convergence diagnostics normal distribution and more on priors
  • Dichotomous variable models measurement and irt models
  • Gibbs sampler and the Metropolis-Hastings algorithm
  • Joint, conditional, marginal distributions
  • latent variable model for ordinal data, Bayes factor, DIC, SSVS
  • Markov chain Monte Carlo methods
  • Metropolis-Hastings algorithm and sampling
  • Model Choice vs. Model Averaging
  • Monte Carlo simulation methods, integration and approximation
  • Multi-parameter models, Multivariate models, Hierarchical model
  • Multivariate normal distribution
  • Ordered and categorical outcomes model checking
  • Posterior distribution and analysis
  • Prior and posterior distributions
  • Sampling methods, Winbugs/jags
  • Sampling: Problems, Techniques, and Post estimation
  • Single and multi-parameter models Gibbs sampling
  • Summarizing posterior distributions
  • The evaluation of posterior and predictive distributions
  • Theory of Bayesian estimation and hypothesis testing

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