Tag Archives: Simulation

Corona Sweden – Herd Immunity matters

Posted on July 1, 2020 by

Sweden, and particularily the Swedish chief epidemiologist Anders Tegnell, as well as his predecessor/mentor Johan Giesecke, have received huge amounts of critizism for the “Swedish Strategy” fighting Corona. A key component of Tegnell’s & Giesecke’s rationale for not recommending Lockdown … Continue reading →

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Corona outbreak – why isolation of infected people matters

Posted on February 13, 2020 by

Just hacked a simple simulation on the spread and effects of a virus, like the Corona virus that is in voque as we speak. The model is very simple, with the following assumptions/constants: The population size is constant, in the … Continue reading →

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Using Bayesian Inference to predict and bet on Italian Serie A Fotball

Posted on September 20, 2018 by

As my old timer readers know, I’be been using Bayesian Inference to predict and bet on various sporting events, such as FIFA World Cup, and IIHF World Championships. With some success. When the Italian premier division started for about a … Continue reading →

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Capturing NMEA sentences over WiFi using Python

Posted on June 16, 2018 by

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 →

Posted in Data Analytics, Maritime Technology, Nautical Information Systems, NMEA, Numpy, performance, Python, Simulation, TCPIP | Tagged , , , , , , , , , , , , | Leave a comment

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

Posted on May 21, 2018 by

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…?

Posted on April 11, 2018 by

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

Posted on April 7, 2018 by

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 →

Posted in Bayes, Big Business, Big Data, Data Analytics, Gambling, Numpy, Probability, PYMC, Python, Simulation, Statistics | Tagged , , , , , , , , , , , | Leave a comment

Bayesian Linear Regression with PYMC, part II

Posted on April 6, 2018 by

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

Posted on April 4, 2018 by

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

Posted on April 1, 2018 by

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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