Mamba.jl - The Julia Programming Language

Mamba: Markov chain Monte Carlo (MCMC) for Bayesian analysis in julia

Purpose

Mamba is an open platform for the implementation and application of MCMC methods to perform Bayesian analysis in julia. The package provides a framework for (1) specification of hierarchical models through stated relationships between data, parameters, and statistical distributions; (2) block-updating of parameters with samplers provided, defined by the user, or available from other packages; (3) execution of sampling schemes; and (4) posterior inference. It is intended to give users access to all levels of the design and implementation of MCMC simulators to particularly aid in the development of new methods.

Several software options are available for MCMC sampling of Bayesian models. Individuals who are primarily interested in data analysis, unconcerned with the details of MCMC, and have models that can be fit in JAGS, Stan, or OpenBUGS are encouraged to use those programs. Mamba is intended for individuals who wish to have access to lower-level MCMC tools, are knowledgeable of MCMC methodologies, and have experience, or wish to gain experience, with their application. The package also provides stand-alone convergence diagnostics and posterior inference tools, which are essential for the analysis of MCMC output regardless of the software used to generate it.

Features

• An interactive and extensible interface.
• Support for a wide range of model and distributional specifications.
• An environment in which all interactions with the software are made through a single, interpreted programming language.
  • Any julia operator, function, type, or package can be used for model specification.
  • Custom distributions and samplers can be written in julia to extend the package.
• Directed acyclic graph representations of models.
• Arbitrary blocking of model parameters and designation of block-specific samplers.
• Samplers that can be used with the included simulation engine or apart from it, including
  • adaptive Metropolis within Gibbs and multivariate Metropolis,
  • approximate Bayesian computation,
  • binary,
  • Hamiltonian Monte Carlo (simple and No-U-Turn),
  • simplex, and
  • slice samplers.
• Automatic parallel execution of parallel MCMC chains on multi-processor systems.
• Restarting of chains.
• Command-line access to all package functionality, including its simulation API.
• Convergence diagnostics: Gelman, Rubin, and Brooks; Geweke; Heidelberger and Welch; Raftery and Lewis.
• Posterior summaries: moments, quantiles, HPD, cross-covariance, autocorrelation, MCSE, ESS.
• Gadfly plotting: trace, density, running mean, autocorrelation.
• Importing of sampler output saved in the CODA file format.
• Run-time performance on par with compiled MCMC software.

Getting Started

The following julia command will install the package:

julia> Pkg.add("Mamba")

See the Package Documentation for details and examples.

Download Details: 
Author: brian-j-smith
Source Code: https://github.com/brian-j-smith/Mamba.jl 
License: MIT
 

#julia #programming #developer

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Mamba.jl - The Julia Programming Language

Mamba: Markov chain Monte Carlo (MCMC) for Bayesian analysis in julia

Purpose

Mamba is an open platform for the implementation and application of MCMC methods to perform Bayesian analysis in julia. The package provides a framework for (1) specification of hierarchical models through stated relationships between data, parameters, and statistical distributions; (2) block-updating of parameters with samplers provided, defined by the user, or available from other packages; (3) execution of sampling schemes; and (4) posterior inference. It is intended to give users access to all levels of the design and implementation of MCMC simulators to particularly aid in the development of new methods.

Several software options are available for MCMC sampling of Bayesian models. Individuals who are primarily interested in data analysis, unconcerned with the details of MCMC, and have models that can be fit in JAGS, Stan, or OpenBUGS are encouraged to use those programs. Mamba is intended for individuals who wish to have access to lower-level MCMC tools, are knowledgeable of MCMC methodologies, and have experience, or wish to gain experience, with their application. The package also provides stand-alone convergence diagnostics and posterior inference tools, which are essential for the analysis of MCMC output regardless of the software used to generate it.

Features

• An interactive and extensible interface.
• Support for a wide range of model and distributional specifications.
• An environment in which all interactions with the software are made through a single, interpreted programming language.
  • Any julia operator, function, type, or package can be used for model specification.
  • Custom distributions and samplers can be written in julia to extend the package.
• Directed acyclic graph representations of models.
• Arbitrary blocking of model parameters and designation of block-specific samplers.
• Samplers that can be used with the included simulation engine or apart from it, including
  • adaptive Metropolis within Gibbs and multivariate Metropolis,
  • approximate Bayesian computation,
  • binary,
  • Hamiltonian Monte Carlo (simple and No-U-Turn),
  • simplex, and
  • slice samplers.
• Automatic parallel execution of parallel MCMC chains on multi-processor systems.
• Restarting of chains.
• Command-line access to all package functionality, including its simulation API.
• Convergence diagnostics: Gelman, Rubin, and Brooks; Geweke; Heidelberger and Welch; Raftery and Lewis.
• Posterior summaries: moments, quantiles, HPD, cross-covariance, autocorrelation, MCSE, ESS.
• Gadfly plotting: trace, density, running mean, autocorrelation.
• Importing of sampler output saved in the CODA file format.
• Run-time performance on par with compiled MCMC software.

Getting Started

The following julia command will install the package:

julia> Pkg.add("Mamba")

See the Package Documentation for details and examples.

Download Details: 
Author: brian-j-smith
Source Code: https://github.com/brian-j-smith/Mamba.jl 
License: MIT
 

#julia #programming #developer

Coding 101: Programming Language Building Blocks

This article will introduce the concepts and topics common to all programming languages, that beginners and experts must know!

Do you want to learn a programming language for the first time?

Do you want to improve as a Programmer?

Well, then you’re in the right place to start. Learn any programming language without difficulty by learning the concepts and topics common to all programming languages.

Let me start by answering the following questions:

• Why learn Programming?
• What is Programming?
• How to Learn a Programming Language?

Why learn Programming❔

Programming develops creative thinking

Programmers solve a problem by breaking it down into workable pieces to understand it better. When you start learning to program, you develop the habit of working your way out in a very structured format. You analyze the problem and start thinking logically and this gives rise to more creative solutions you’ve ever given.

Whether you want to uncover the secrets of the universe, or you just want to pursue a career in the 21st century, basic computer programming is an essential skill to learn.

_– _Stephen Hawking

Everybody in this country should learn how to program a computer… because it teaches you how to think.

_- _Steve Jobs

Programming Provides Life-Changing Experiences

Programming always provides you with a new challenge to take risks every time and that teaches you to take risks in your personal life too. The world is filled up with websites, apps, software and when you build these yourself you’ll feel more confident. When a programmer solves a problem that no one has ever solved before it becomes a life-changing experience for them.

What is Programming🤔?

A program is a set of instructions to perform a task on a computer.

Programming is the process of designing and building an executable computer program to accomplish a specific task.

Well, according to me programming is like raising a baby. We provide knowledge (data) to help understand a baby what’s happening around. We teach a baby to be disciplined (and much more) by making rules.

Similarly, a computer is like a baby. We set rules and provide data to the computer through executable programs with the help of a Programming Language.

(Photo by Clément H on Unsplash)

That’s it👍. If you can understand this basic concept of programming, you’re good to go. Pick up a programming language and start learning. Read the following section to get an idea of where to start.

My recommendation is to choose Python Programming Language as a start, because it’s beginner-friendly.

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