arviz VS faker

pymc3 is the standard Python library for Bayesian statistics, and used ArviZ for plotting, built on top of matplotlib

This tutorial explains what is probabilistic programming & provides a review of 5 frameworks (PPLs) using an example taken from Chapter 4 of Statistical Rethinking by Dr. Richard McElreath. Frameworks (PPLs) reviewed are - Stan (https://mc-stan.org/) PyMC3 (https://docs.pymc.io/) Tensorflow Probability (https://www.tensorflow.org/probability) Pyro/NumPyro (https://pyro.ai/) Turing.jl (https://turing.ml/stable/) I also provide the basic review of a great library called arviz (https://arviz-devs.github.io/arviz/), which can be used for all the above-mentioned PPLs to do Exploratory Data Analysis of Bayesian Models. Here is the link to the notebook in which I have implemented the example model using the above Frameworks/PPLs https://colab.research.google.com/drive/1zgR2b0j2waGi1ppnIe1rw7emkbBXtMqF?usp=sharing

https://github.com/arviz-devs/arviz Exploratory analysis of Bayesian models with Python

While we’re at it, let's add a couple of other gems we’ll need for our test environment: factory_bot_rails is a fixtures replacement and generates test model instances. faker is handy for generating fake strings of data to be used in tests. Add those gems to the development and test group of your Gemfile:

Next up, we'll combine a few of the techniques we've reviewed to implement snappy typeahead searching. Before we do that, though, let's generate more sample data. I will use the popular faker gem to do that:

Faker was originally written in Perl and is also available as a library for Ruby, Java, and Python.

Faker

rspec-rails factory_bot_rails faker

Ah, so the data is just a giant .yml file of potential options, that I compiled, and then faker grabs a random one based on that data. There's no "database" or anything akin to that.

It's with the Faker library. I was able to reproduce it by spinning up a new rails up, specifying the faker gem , yours, and running the command gives the same error.

To begin using faker, you need to install the gem, which is simply done by running "gem install faker". After you do that, you should add "gem "faker"" to your gem file to ensure correct usage, otherwise you may get an error. Once thats done you can head over to the seeding file and add require "faker" the page. In the seeding file is where you would start to "fake" your data and we use the faker library to pick out random dummy data. In the seeding, your gonna start implementing the data which should look like this,