Stan tutorial for beginners in ~6 mins: Bayesian Data Analysis Software

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Stan is a probabilistic programming language for Bayesian inference. Get the codes used in this video here: https://sites.google.com/a/ehsankarim.com/www/software/rstancodes.zip?attredirects=0&d=1 Eight schools example is also discussed in Andrew Gelman's blog: http://andrewgelman.com/2014/01/21/everything-need-know-bayesian-statistics-learned-eight-schools/ For contacting me, here is my website: http://www.ehsankarim.com/ - feel free to email me for possible research collaboration opportunities.

Stan is for statistical modeling, data analysis, and prediction, and a probabilistic programming language that can do full Bayesian statistical inference with MCMC sampling, approximate Bayesian inference with variational inference and penalized maximum likelihood estimation with optimization (L-BFGS). Added R packages provide expression-based linear modeling, posterior visualization, and leave-one-out cross-validation. It does efficient Bayesian inference with Hamiltonian Monte Carlo.

Keywords: Stan Modeling Language Regression Models Time-Series Models Missing Data & Partially Known Parameters Truncated or Censored Data Finite Mixtures Measurement Error and Meta-Analysis Latent Discrete Parameters Sparse and Ragged Data Structures Clustering Models Gaussian Processes Directions, Rotations, and Hyperspheres Solving Differential Equations Reparameterization & Change of Variables Custom Probability Functions User-Defined Functions Problematic Posteriors Matrices, Vectors, and Arrays Multiple Indexing and Range Indexing Optimizing Stan Code for Efficiency Inference Bayesian Data Analysis Markov Chain Monte Carlo Sampling Penalized Maximum Likelihood Point Estimation Bayesian Point Estimation Variational Inference Algorithms & Implementations Hamiltonian Monte Carlo Sampling Transformations of Constrained Variables Optimization Algorithms Variational Inference Diagnostic Mode Built-In Functions Void Functions Integer-Valued Basic Functions Real-Valued Basic Functions Array Operations Matrix Operations Sparse Matrix Operations Mixed Operations Ordinary Differential Equation Solvers Discrete Distributions Conventions for Probability Functions Binary Distributions Bounded Discrete Distributions Unbounded Discrete Distributions Multivariate Discrete Distributions Continuous Distributions Unbounded Continuous Distributions Positive Continuous Distributions Non-negative Continuous Distributions Positive Lower-Bounded Probabilities Continuous Distributions Circular Distributions Bounded Continuous Probabilities Distributions over Unbounded Vectors Simplex Distributions Correlation Matrix Distributions Covariance Matrix Distributions Software Development Model Building as Software Development Software Development Lifecycle Reproducibility Contributed Modules Stan Program Style Guide