Bayesian Regression in PYMC3 using MCMC & Variational Inference

Posted on Wed 07 November 2018 in data-science • Tagged with machine-learning, probabilistic-programming, python, pymc3

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Conducting a Bayesian data analysis - e.g. estimating a Bayesian linear regression model - will usually require some form of Probabilistic Programming Language (PPL), unless analytical approaches (e.g. based on conjugate prior models), are appropriate for the task at hand. More often than not, PPLs implement Markov Chain Monte Carlo …


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Building a Data Science Platform for R&D, Part 4 - Apache Zeppelin & Scala Notebooks

Posted on Mon 29 August 2016 in data-science • Tagged with AWS, data-processing

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Parts one, two and three of this series of posts have taken us from creating an account on AWS to loading and interacting with data in Spark via R and R Studio. My vision of a Data Science platform for R&D is nearly complete - the only outstanding component is …


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Building a Data Science Platform for R&D, Part 3 - R, R Studio Server, SparkR & Sparklyr

Posted on Mon 22 August 2016 in data-science • Tagged with AWS, data-processing, apache-spark

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Part 1 and Part 2 of this series dealt with setting up AWS, loading data into S3, deploying a Spark cluster and using it to access our data. In this part we will deploy R and R Studio Server to our Spark cluster’s master node and use it to …


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Building a Data Science Platform for R&D, Part 2 - Deploying Spark on AWS using Flintrock

Posted on Thu 18 August 2016 in data-science • Tagged with AWS, data-processing, apache-spark

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Part 1 in this series of blog posts describes how to setup AWS with some basic security and then load data into S3. This post walks-through the process of setting up a Spark cluster on AWS and accessing our S3 data from within Spark.

A key part of my vision …


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Building a Data Science Platform for R&D, Part 1 - Setting-Up AWS

Posted on Tue 16 August 2016 in data-science • Tagged with AWS, data-processing

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Here’s my vision: I get into the office and switch-on my laptop; then I start-up my Spark cluster; I interact with it via RStudio to exploring a new dataset a client uploaded overnight; after getting a handle on what I want to do with it, I prototype an ETL …


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