Merge pull request #111 from TuringLang/tor/redirects

Added redirects

markdown/00-introduction/00_introduction.md

@@ -1,4 +1,5 @@
 ---
+redirect_from: "tutorials/0-introduction/"
 title: "Introduction to Turing"
 permalink: "/:collection/:name/"
 ---

markdown/01-gaussian-mixture-model/01_gaussian-mixture-model.md

@@ -1,4 +1,5 @@
 ---
+redirect_from: "tutorials/1-gaussianmixturemodel/"
 title: "Unsupervised Learning using Bayesian Mixture Models"
 permalink: "/:collection/:name/"
 ---

markdown/02-logistic-regression/02_logistic-regression.md

@@ -1,4 +1,5 @@
 ---
+redirect_from: "tutorials/2-logisticregression/"
 title: "Bayesian Logistic Regression"
 permalink: "/:collection/:name/"
 ---

markdown/03-bayesian-neural-network/03_bayesian-neural-network.md

@@ -1,4 +1,5 @@
 ---
+redirect_from: "tutorials/3-bayesnn/"
 title: "Bayesian Neural Networks"
 permalink: "/:collection/:name/"
 ---

markdown/04-hidden-markov-model/04_hidden-markov-model.md

@@ -1,4 +1,5 @@
 ---
+redirect_from: "tutorials/4-bayeshmm/"
 title: "Bayesian Hidden Markov Models"
 permalink: "/:collection/:name/"
 ---

markdown/05-linear-regression/05_linear-regression.md

@@ -1,4 +1,5 @@
 ---
+redirect_from: "tutorials/5-linearregression/"
 title: "Linear Regression"
 permalink: "/:collection/:name/"
 ---

markdown/06-infinite-mixture-model/06_infinite-mixture-model.md

@@ -1,4 +1,5 @@
 ---
+redirect_from: "tutorials/6-infinitemixturemodel/"
 title: "Probabilistic Modelling using the Infinite Mixture Model"
 permalink: "/:collection/:name/"
 ---

markdown/07-poisson-regression/07_poisson-regression.md

@@ -1,4 +1,5 @@
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+redirect_from: "tutorials/7-poissonregression/"
 title: "Bayesian Poisson Regression"
 permalink: "/:collection/:name/"
 ---

markdown/08-multionomial-regression/08_multinomial-logistic-regression.md

@@ -1,4 +1,5 @@
 ---
+redirect_from: "tutorials/8-multinomiallogisticregression/"
 title: "Bayesian Multinomial Logistic Regression"
 permalink: "/:collection/:name/"
 ---

markdown/09-variational-inference/09_variational-inference.md

@@ -1,4 +1,5 @@
 ---
+redirect_from: "tutorials/9-variationalinference/"
 title: "Variational inference (VI) in Turing.jl"
 permalink: "/:collection/:name/"
 ---

markdown/10-bayesian-differential-equations/10_bayesian-differential-equations.md

@@ -1,4 +1,5 @@
 ---
+redirect_from: "tutorials/10-bayesiandiffeq/"
 title: "Bayesian Estimation of Differential Equations"
 permalink: "/:collection/:name/"
 ---

tutorials/00-introduction/00_introduction.jmd

@@ -1,6 +1,7 @@
 ---
 title: Introduction to Turing
 permalink: /:collection/:name/
+redirect_from: tutorials/0-introduction/
 ---
 
 ## Introduction

tutorials/01-gaussian-mixture-model/01_gaussian-mixture-model.jmd

@@ -1,6 +1,7 @@
 ---
 title: Unsupervised Learning using Bayesian Mixture Models
 permalink: /:collection/:name/
+redirect_from: tutorials/1-gaussianmixturemodel/
 ---
 
 The following tutorial illustrates the use *Turing* for clustering data using a Bayesian mixture model. The aim of this task is to infer a latent grouping (hidden structure) from unlabelled data.

tutorials/02-logistic-regression/02_logistic-regression.jmd

@@ -1,6 +1,7 @@
 ---
 title: Bayesian Logistic Regression
 permalink: /:collection/:name/
+redirect_from: tutorials/2-logisticregression/
 ---
 [Bayesian logistic regression](https://en.wikipedia.org/wiki/Logistic_regression#Bayesian) is the Bayesian counterpart to a common tool in machine learning, logistic regression. The goal of logistic regression is to predict a one or a zero for a given training item. An example might be predicting whether someone is sick or ill given their symptoms and personal information.
 

tutorials/03-bayesian-neural-network/03_bayesian-neural-network.jmd

@@ -1,6 +1,7 @@
 ---
 title: Bayesian Neural Networks
 permalink: /:collection/:name/
+redirect_from: tutorials/3-bayesnn/
 ---
 
 In this tutorial, we demonstrate how one can implement a Bayesian Neural Network using a combination of Turing and [Flux](https://github.com/FluxML/Flux.jl), a suite of tools machine learning. We will use Flux to specify the neural network's layers and Turing to implement the probabalistic inference, with the goal of implementing a classification algorithm.

tutorials/04-hidden-markov-model/04_hidden-markov-model.jmd

@@ -1,6 +1,7 @@
 ---
 title: Bayesian Hidden Markov Models
 permalink: /:collection/:name/
+redirect_from: tutorials/4-bayeshmm/
 ---
 This tutorial illustrates training Bayesian [Hidden Markov Models](https://en.wikipedia.org/wiki/Hidden_Markov_model) (HMM) using Turing. The main goals are learning the transition matrix, emission parameter, and hidden states. For a more rigorous academic overview on Hidden Markov Models, see [An introduction to Hidden Markov Models and Bayesian Networks](http://mlg.eng.cam.ac.uk/zoubin/papers/ijprai.pdf) (Ghahramani, 2001).
 

tutorials/05-linear-regression/05_linear-regression.jmd

@@ -1,6 +1,7 @@
 ---
 title: Linear Regression
 permalink: /:collection/:name/
+redirect_from: tutorials/5-linearregression/
 ---
 
 Turing is powerful when applied to complex hierarchical models, but it can also be put to task at common statistical procedures, like [linear regression](https://en.wikipedia.org/wiki/Linear_regression). This tutorial covers how to implement a linear regression model in Turing.

tutorials/06-infinite-mixture-model/06_infinite-mixture-model.jmd

@@ -1,6 +1,7 @@
 ---
 title: Probabilistic Modelling using the Infinite Mixture Model
 permalink: /:collection/:name/
+redirect_from: tutorials/6-infinitemixturemodel/
 ---
 
 In many applications it is desirable to allow the model to adjust its complexity to the amount the data. Consider for example the task of assigning objects into clusters or groups. This task often involves the specification of the number of groups. However, often times it is not known beforehand how many groups exist. Moreover, in some applictions, e.g. modelling topics in text documents or grouping species, the number of examples per group is heavy tailed. This makes it impossible to predefine the number of groups and requiring the model to form new groups when data points from previously unseen groups are observed.

tutorials/07-poisson-regression/07_poisson-regression.jmd

@@ -1,6 +1,7 @@
 ---
 title: Bayesian Poisson Regression
 permalink: /:collection/:name/
+redirect_from: tutorials/7-poissonregression/
 ---
 This notebook is ported from the [example notebook](https://docs.pymc.io/notebooks/GLM-poisson-regression.html) of PyMC3 on Poisson Regression.  
 

tutorials/08-multionomial-regression/08_multinomial-logistic-regression.jmd

@@ -1,6 +1,7 @@
 ---
 title: Bayesian Multinomial Logistic Regression
 permalink: /:collection/:name/
+redirect_from: tutorials/8-multinomiallogisticregression/
 ---
 [Multinomial logistic regression](https://en.wikipedia.org/wiki/Multinomial_logistic_regression) is an extension of logistic regression. Logistic regression is used to model problems in which there are exactly two possible discrete outcomes. Multinomial logistic regression is used to model problems in which there are two or more possible discrete outcomes.
 

tutorials/09-variational-inference/09_variational-inference.jmd

@@ -1,6 +1,7 @@
 ---
 title: Variational inference (VI) in Turing.jl
 permalink: /:collection/:name/
+redirect_from: tutorials/9-variationalinference/
 ---
 
 In this post we'll have a look at what's know as **variational inference (VI)**, a family of _approximate_ Bayesian inference methods, and how to use it in Turing.jl as an alternative to other approaches such as MCMC. In particular, we will focus on one of the more standard VI methods called **Automatic Differentation Variational Inference (ADVI)**.

tutorials/10-bayesian-differential-equations/10_bayesian-differential-equations.jmd

@@ -1,6 +1,7 @@
 ---
 title: Bayesian Estimation of Differential Equations
 permalink: /:collection/:name/
+redirect_from: tutorials/10-bayesiandiffeq/
 ---
 
 Most of the scientific community deals with the basic problem of trying to mathematically model the reality around them and this often involves dynamical systems. The general trend to model these complex dynamical systems is through the use of differential equations. Differential equation models often have non-measurable parameters. The popular “forward-problem” of simulation consists of solving the differential equations for a given set of parameters, the “inverse problem” to simulation, known as parameter estimation, is the process of utilizing data to determine these model parameters. Bayesian inference provides a robust approach to parameter estimation with quantified uncertainty.