Simple Bayesian Modeling on Edward by Hamiltonian Monte Carlo

Overview

On this article, I'll re-write by Edward the simple bayesian model that was written on Stan.
This time, my target is from the following article.

• Simple Bayesian modeling by Stan

Actually, I already wrote the article, Simple regression by Edward: variational inference. There, I re-wrote the model on Edward. But at that time, I used variational bayesian method for inference.
Stan uses Hamiltonian Monte Carlo. So, this time, I'll use Hamiltonian Monte Carlo on Edward and re-write the model.

Edward modeling to an artificial data

Overview

On the article below, I switched method on Edward model from variational method to Hamiltonian Monte Carlo.

• Hamiltonian Monte Carlo on TensorFlow and Edward

As an another example, I'll try same thing to the model of the following article.

• Edward modeling to artificial data with random effects

In a nutshell, I'll make model for an artificial data and get sample by Hamiltonian Monte Carlo.

Hamiltonian Monte Carlo on TensorFlow and Edward

Overview

On this article, I tried Hamiltonian Monte Carlo algorithm to the simple data by TensorFlow and Edward.
Edward lets us use variational inference, Gibbs sampling and Monte Carlo method. And by relatively small changes, we can switch the methods. So I'll try simple HML model here.
About Hamiltonian Monte Carlo itself, I'll write another article for it.

Time series analysis on TensorFlow and Edward: local level model:P.S. 1

Overview

On the article below, I tried to analyze time series data with local level model. On Stan, I could do it before without problem. But on Edward and TensorFlow, I have been struggling.

Time series analysis on TensorFlow and Edward: local level model

Deep learning and Machine learning methods blog

From the situation above, although it doesn’t work well yet, I got some progress.

Time series analysis on TensorFlow and Edward: local level model

Overview

To review the time series analysis from the basic points, I tried to do state space modeling with TensorFlow and Edward. And I’m at a loss.
The main purposes are these two.

• review the time series analysis from the basic points
• try to check how to do that on Edward and TensorFlow

Edward modeling to artificial data with random effects

Overview

By Edward, I’ll try to make the model with random effect.
There are some ways to fulfill that. On this article, I’ll follow the style that the Edward tutorial takes.

• Edward Tutorial

Fashion-MNIST exploring using Keras and Edward

Overview

On the article, Fashion-MNIST exploring, I concisely explored Fashion-MNIST dataset.

We can get access to the dataset from Keras and on this article, I’ll try simple classification by Edward.

Simple Baysian Neural Network with Edward

Overview

Edward can enable us to convert TensorFlow code to Baysian one. I’m not used to Edward. So for the training, I’m tackling with converting some TensorFlow code to Edward one. On this article, I tried to convert simple neural network model to Baysian neural network one.

The purpose of this article is to convert the TensorFlow code I posted before to Baysian one by Edward.

Baysian neural network model

By Edward, we can relatively easily convert the model using TensorFlow to probabilistic one.
The regression model for iris data is from the article below.

Simple regression model by TensorFlow

Neural network is composed of input, hidden and output layers. And the number of hidden layers is optional. So the simplest network architecture has just one hidden layer. On this article, I'll make the simplest neural network for regression by TensorFlow.

In a nutshell, the model is to predict one target value from three features. About the details, please check the article.

Simple regression by Edward: variational inference

Overview

Edward is one of the PPL(probabilistic programming language). This enables us to use variational inference, Gibbs sampling and Monte Carlo method relatively easily. But it doesn’t look so easy. So step by step, I’ll try this.

On this article, simple regression, tried on the article Simple Bayesian modeling by Stan, can be the nice example. So I did same things by Edward, using variational inference.