Tag Archives: Simulation

Capturing NMEA sentences over WiFi using Python

In order to figure out how the NMEA-WiFi Gateway deals with clients, e.g. if it expects any “handshake” or any other communication setup protocol, I decided to write a simulator mimicing the gateway, and then using iRegatta 2 from Zifago … Continue reading

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Making a living as a Professional Scientific Gambler using Bayesian Inference…?

As my readers know, over the past few weeks I’ve been conducting an experiment: Applying scientific betting on the just finished Ice Hockey World Championships.  By “scientific”, I’m referring to the exclusive use of statistical and mathematical models, simulation, and … Continue reading

Posted in Bayes, Data Analytics, Data Driven Management, Finance, Gambling, HOCKEY-2018, Math, Numpy, Probability, PYMC, Python, Simulation, Statistics | Tagged , , , , , , , , , | Leave a comment

Bayesian Inference – what good is the Prior, anyway…?

A brief example on the effect of Bayesian priors (I’m going to use my Ice Hockey Championship Prediction hack under development for this example): Assume you would like to bet on the outcome of some particular game, for instance, Sweden … Continue reading

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Predicting the outcome of World Championships in Ice Hockey using Bayesian Inference

Just for fun, I thought I’d implement a Bayesian “statistical inference engine” for some sports tournament. For whatever reason, I came to choose ice hockey world championships, training my inference engine on data from the 2016 tournament, and testing its … Continue reading

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Bayesian Linear Regression with PYMC, part II

In the previous post we looked at a simple linear regression between (simulated) human heights and weights.  In that example, the regression was truly ‘linear’, in that the predictor variable only occured in its first power, to provide a linear regression … Continue reading

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Bayesian Linear Regression with PYMC

Is there a relationship between human height and weight…? Probably. But what does that relationship look like ? Those kinds of questions can be answered by Linear Regression. Below an example, using simulated data for weights,heights etc: import numpy as … Continue reading

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Python,Pandas, Statsmodels: Linear Regression – dealing with categorical data

Sometimes your explanatory variables are not numeric, but categorical. Typical example: “Gender” – which used to be binary, and since I’m an old man, I will continue regarding it as binary… 😉 To handle categorical data in Linear Regression, we … Continue reading

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Val2018 – prognos uppdaterad med opinionssiffrorna för mars [Bayesian Inference]

Inför mars månads prognos har jag gjort flera ändringar i min prognos-modell: Modellen har nu meta-parametrar, i form av en distribution baserad på en uppskattad, subjektiv “valvind” eller “sentiment”, dvs modellen är nu hierarkisk. Modellen bygger nu enbart på de … Continue reading

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Distribution of distributions in 3D

Just a quick add-on to my previous post on yet another way to present multidimensional data: To recap, we have a “distribution of distributions”, where each distribution has two dimensions, mu and sigma. In the previous post, I chose to present … Continue reading

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Tiny Data – Bayesian dirty socks – but how many were there in the laundry machine…?

I found this very illuminating short tutorial video on Approximate Bayesian Computation, by Rasmus Bååth, on youtube, and since Rasmus example uses R as the implementation language, I decided to implement the example in Python. The problem at hand is … Continue reading

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