Stochastic Process Calibration using Bayesian Inference & Probabilistic Programs

Posted on Fri 18 January 2019 in data-science • Tagged with probabilistic-programming, python, pymc3, quant-finance, stochastic-processes

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Stochastic processes are used extensively throughout quantitative finance - for example, to simulate asset prices in risk models that aim to estimate key risk metrics such as Value-at-Risk (VaR), Expected Shortfall (ES) and Potential Future Exposure (PFE). Estimating the parameters of a stochastic processes - referred to as ‘calibration’ in the parlance …


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Deploying Python ML Models with Flask, Docker and Kubernetes

Posted on Thu 10 January 2019 in machine-learning-engineering • Tagged with python, machine-learning, machine-learning-operations, kubernetes, docker, GCP

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A common pattern for deploying Machine Learning (ML) models into production environments - e.g. a ML model trained using the SciKit-Learn package in Python and ready to provide predictions on new data - is to expose them as RESTful API microservices hosted from within Docker containers, that are in-turn deployed to …


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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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