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Coursework pertaining to CS5350 : Bayesian Data Analysis offered in Spring 2019

Python 100.00%
bayesian-inference monte-carlo-methods variational-inference

pump-failure-prediction's Introduction

Pump Failure Prediction

What is the goal of the problem?

  • You have 10 power plants pumps. For these 10 pumps, you are given the number of failures (xi) and the number of hours in thousands that the pumps have run for (ti).

  • We seek to model the relationship between xi and ti using a Gamma-Poisson Hierarchical model as done by George et al in their paper Conjugate Likelihood Distributions in 1993.

What is the goal of the assignment?

  • We will be solving this problem using Metropolis-Hastings algorithm - a popular variant of the Monte-Carlo Markov Chain (MCMC) algorithm, which will be implemented natively using "standard" libraries.

  • Our implementation will then be compared against an implementation in PyMC3, a popular probabilistic programming framework.

  • The sampling algorithm will be compared against an variational inference algorithm which is optimization based. This has been implemented using PyMC3 as well.

Further details

  • Further details of the problem and comparison of implementations are presented in the report.

Running the code

  • Please install the requirements specified in requirements.txt. To use the PyMC3 implementations, you will need to install PyMC3.
  • python <script> --help will print a set of configurable options while running the code where <script> is one of [numpy_mcmc.py, pymc3_mcmc.py, pymc3_vi.py].
  • The dataset for the assignment is here

References


Done as part of coursework pertaining to CS5350 : Bayesian Data Analysis offered in Spring 2019

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