Tag Archives: Numpy

Tour de Ski Final climb – does age matter for performance ?

In an earlier post, I analyzed data from the Marcialonga Ski race. Marcialonga is one of the classic long distance ski races, where both elite’ as well as amateurs compete together. In fact, the vast majority of the competitors in … Continue reading

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Marcialonga Ski 2018 – some Analytics

Now, with the power grid finally – after 62 hours! – back in business, I’m able to continue my stats/analytics exploration of the past Marcialonga ski race. First, some basic stats about the race: Total number of participants: 5558, of … Continue reading

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Bayesian Linear Regression with PYMC

Python, Pandas & PYMC example on Bayesian Linear Regression, adopted from Richard McElreath’s “Statistical Rethinking” class, where he uses R as modeling language instead of PYMC. Data in a csv-file describe various attributes such as weight, height, age, gender etc … Continue reading

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Python & Pandas to map gps coordinates to known locations

Assume you have a gps log file, with time and position (Lat,Lon in columns 9,10) info, like: 2018.12.12 00:41:20;0;0;0;0;0;1;0;25.8;59.348978;17.969643;0;0; 2018.12.12 01:41:21;0;0;0;0;0;1;0;25.7;59.348962;17.969627;0;0; 2018.12.12 02:41:21;0;0;0;0;0;1;0;25.7;59.349;17.969688;0;0; 2018.12.12 03:41:21;0;0;0;0;0;1;0;25.7;59.349;17.96966;0;0; 2018.12.12 04:41:22;0;0;0;0;0;1;0;25.6;59.349007;17.969618;0;0; 2018.12.12 04:48:50;0;0;0;0;1;1;1;25.2;59.349007;17.969635;0;0; 2018.12.12 04:49:51;0;0.001;0;0;1;1;1;28.3;59.349;17.969642;0;0; Assume further that you’d like to map each of … Continue reading

Posted in Maritime Technology, Nautical Information Systems, Numpy, Pandas, Python | Tagged , , , | Leave a comment

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

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

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

Bayesian Multi-predictor Regression – Valet2018

[Continuing my exploration of the Swedish election results, but I thought this might be of interest also for those of you not very interested in the Swedish elections, simply because the potential MatStat’s  insights – thus, the text is in … Continue reading

Posted in Bayes, Big Data, Data Analytics, Data Driven Management, Numpy, Politik, Probability, PYMC, Python, Research, Society, Statistics, Sverige | Tagged , , , , , , , , , , | 1 Comment

PYMC – Markov Chain Monte Carlo regression – canonical example

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Val2018 Bayesian Inference – sammanfattning

Nu är ju inte den slutgiltiga rösträkningen klar, men resultatet ur ett statistiskt / matematiskt perspektiv är ändå så stabilt att jag väljer att summera mina resultat redan nu. I graferna nedan har jag använt mig av samtliga opinionsinstituts prognoser … Continue reading

Posted in Bayes, Data Analytics, Functional Stupidity, Politik, Probability, PYMC, Society, Statistics, Sverige | Tagged , , , , , , , , , , , , | Leave a comment

Val2018 – sista prognosen

Första grafen: Bayesian Inference över samtliga opinionsinstituts mätningar augusti-september 2018. Andra grafen: samma rådata som ovan, obearbetat. Stora skuggade stapeln för respektive parti anger valresultatet 2014.  Svarta tunna staplarna är 89:e percentilen.

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Val2018 – Bayesian Linear Regression, senaste opinionsmätningarna

Med endast en dag kvar till valet kan det vara intressant att titta på trenden på de olika opinionsinstitutens mätningar från de senaste två månaderna, fram till och med 2018/09/06. Inom “vanlig” statistik, så är en trendlinje just det, *en* … Continue reading

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