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Refactor Manual #354

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betanalpha opened this issue Nov 2, 2013 · 3 comments
Closed

Refactor Manual #354

betanalpha opened this issue Nov 2, 2013 · 3 comments
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@betanalpha
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We really need to refactor the manual so that we have separate sections describing our samplers/optimizers and their configurations. I'm thinking a chapter after "Introduction" called something like "Algorithms" with details on HMC, adaptation, optimization, and potential pitfalls in practice, which would leave the configuration doc in the "Commands" chapter much cleaner.

I assign myself since I know the most about the samplers.

@ghost ghost assigned betanalpha Nov 2, 2013
@bob-carpenter
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I heartily concur. Thanks for taking it on.

You'll see some material on samplers scattered throughout.

I kept planning to reorganize the material as soon as we got
more than one sampler in place.

  • Bob

On 11/2/13, 7:21 PM, Michael Betancourt wrote:

We really need to refactor the manual so that we have separate sections describing our samplers/optimizers and their
configurations. I'm thinking a chapter after "Introduction" called something like "Algorithms" with details on HMC,
adaptation, optimization, and potential pitfalls in practice, which would leave the configuration doc in the "Commands"
chapter much cleaner.

I assign myself since I know the most about the samplers.


Reply to this email directly or view it on GitHub #354.

@betanalpha
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Roadmap

Bayesian Inference in Practice

  • Uncertainty as Probability, Bayes Theorem
    • Likelihood, Prior, Posterior
  • Employing the Posterior with Expectations
    • Measures and Densities
    • Typical Set, Integrals Hard
  • Computational Techniques
    • Approximating Expectations with Point Estimates
    • Markov Chain Monte Carlo
      • Ergodicity, Geometric Ergodicity
        • Convergence and Mixing
        • The Monte Carlo Central Limit Theorem
      • Convergence in Practice
        • Finding the typical set
        • Necessary conditions as proxy for sufficient conditions
          • Split Rhat
      • Analysis
        • Autocorrelations
        • Effective Sample Size
        • Monte Carlo Standard Error
      • Markov Kernels
        • Random Walk Metropolis
        • Gibbs (BUGS, JAGS)
        • Hamiltonian Monte Carlo
          Stan
  • The Stan Modeling Language
    • Example
    • Types and Declarations
    • Expressions
    • Statements
    • Program Blocks
    • Functions
  • Automatic Differentiation
    • Factoring algebra from partials with dual numbers
    • Examples
    • Implementation
  • The Stan Algorithms
    • Optimization
      • BFGS, L-BFGS
    • MCMC
      • HMC

Stan in Practice

  • Best Practices
    • Development
    • Style
  • Optimization
    • Model Implementation
      • Rao-Blackwellization
      • Reparameterization
    • Stan Implementation
      • Linear Algebra
      • Vectorization
  • Modeling Techniques
    • Dump from Stan

The Stan Reference Guide

  • Modeling Language
    • BNF Syntax
    • Functions
    • Distributions
      • Continuous
      • Discrete
  • Algorithms
    • Optimization
      • BFGS, L-BFGS
        • Configurations
    • MCMC
      • HMC
        • Configurations

The Stan Interfaces

  • CmdStan, RStan, PyStan, MStan
    • Data Format
    • Compiling Models
    • Running Models
    • Tools
    • Quick Start Guide

The Stan Development Process

  • Tools
  • Issues
  • Pull Requests
  • Submitting a Patch

@bob-carpenter
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absorbed into #2103

@syclik syclik modified the milestones: Future, v2.13.0 Nov 25, 2016
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