Professional Certificate Bayesian Statistics R

Name: Professional Certificate Bayesian Statistics R
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The Professional Certificate in Bayesian Statistics from <a href="https://www.edx.

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org/professional-certificate/bayesian-statistics-and-decision-making">edX is a game-changer for those seeking to harness the power of Bayesian methods in data analysis. This program emphasizes the importance of probability theory, decision theory, and statistical models, enabling learners to make informed decisions based on data. With a strong demand for Bayesian statisticians across various industries such as finance, healthcare, and technology, this course equips learners with essential skills to meet industry needs. Learners will master the use of the probabilistic programming language Stan and its associated R package rstan for Bayesian modeling and inference. By completing this course, learners will enhance their career opportunities, demonstrate expertise in Bayesian statistics, and strengthen their ability to apply statistical methods to real-world problems. This program is an excellent investment for those looking to advance their skills and stand out in the competitive job market.

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• Introduction to Bayesian Statistics: Basic concepts, principles, and benefits of Bayesian statistics. Understanding probability, likelihood, and prior distributions.
• Probability Distributions in R: Overview of probability distributions, focusing on discrete and continuous distributions in R. Generating random variables and visualizing distributions.
• Bayes' Theorem in R: Implementing Bayes' theorem in R, deriving posterior distributions, and understanding the role of prior and likelihood.
• Conjugate Priors in Bayesian Analysis: Introduction to conjugate priors, selecting appropriate conjugate priors in R, and demonstrating their benefits in Bayesian analysis.
• MCMC & Gibbs Sampling: Overview of Markov Chain Monte Carlo (MCMC) and Gibbs sampling methods, implementing MCMC and Gibbs sampling in R, and understanding their role in Bayesian analysis.
• Model Checking & Comparison: Evaluating and comparing Bayesian models in R, assessing model fit, and ensuring model assumptions are met.
• Bayesian Hierarchical Modeling: Understanding the concept of hierarchical modeling, implementing hierarchical models in R, and interpreting posterior distributions.
• Applications of Bayesian Statistics: Exploring real-world applications of Bayesian statistics in various fields, demonstrating how to apply Bayesian methods to solve complex problems using R.

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ProficiencyEnglish
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FastTrack GBP £140

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AcceleratedLearningPath

ThreeFourHoursPerWeek
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StandardMode GBP £90

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TwoThreeHoursPerWeek
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PROFESSIONAL CERTIFICATE BAYESIAN STATISTICS R

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London School of International Business (LSIB)

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05 May 2025

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