Measuring the hay in the haystack: quantifying hidden variables using Bayesian Inference

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Technology-driven trading is a field with many challenges, andperformance and availability of the network communication is essentialto the business. To have a good understanding on the performance andavailability, we monitor certain metrics - however not every interestingmetric is readily available to measure. Some of these have to beinferred from the data we see in production by incorporating our ownknowledge. What complicates this further is that the relationshipbetween the hidden variables and the output data is not a deterministicone, as we are often dealing with a stochastic system.Bayesian inference is a suitable way to tackle this issue - it allowsencoding our knowledge as a prior distribution of the model parameters.Here we will go through real-world uses of Bayesian inference at IMC,using PyMC3 to make an estimate for the hidden metrics in the networktraffic.Knowledge: No prior knowledge of PyMC3 is required. Since this is ashort presentation, the talk with approach the problem and the solutionat a high level instead of implementation details.

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