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Educate-Inspire-Change
Empty Suit
Definition: An amateur who's been given the power to set the goals & objectives for professionals to follow, and thinks of himself as a strong leader.Ethics
"Veritas vos liberabit"
"It's better to build something 100 people love, than something that 1M people kind-of-like" -- Brian Chesky
"Rättvisa handlar inte om lika utfall, utan om lika spelregler" -- Alice Teodorescu
“The mediocre teacher tells. The good teacher explains. The superior teacher demonstrates. The great teacher inspires.” ― William Arthur Ward
When the facts change, I change my mind. What do you do, sir?" -- John Maynard Keynes
""Don't attribute to stupidity what can be adequately explained by a contract." -- Anonymous
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"I do not agree with what you have to say, but I'll defend to the death your right to say it." -- VoltaireSystems view
"Our organization was designed for a problem that no longer existed; we had brought an industrial age force to an information-age conflict." --Stan McChrystal- A/B Testing
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Tag Archives: Probability
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
Posted in Bayes, Data Analytics, Math, Numpy, Pandas, Probability, PYMC, Python, SNA, sports, Statistics
Tagged Bayes, Matplotlib, Numpy, Probability, PYMC, Python, Scipy, sports, Statistics
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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
Posted in Bayes, Data Analytics, Numpy, Pandas, Probability, PYMC, Python, sports, Statistics
Tagged Bayes, Data Analysis, Numpy, Pandas, Probability, PYMC, Python, sports, Statistics, WebScraping
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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
Posted in Bayes, Data Analytics, Numpy, Pandas, Probability, PYMC, Python, Statistics
Tagged Bayes, Matplotlib, Numpy, Pandas, Probability, PYMC, Python, Statistics
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Bayesian Inference – the dangers of “small data” combined with a mis-informed Prior
Continuing on my previous example of trying to figure out the true proportion of blue marbles in a bag: Previously, we used a non-informative, uniform prior for our inference. This time, let’s compare that non-informed prior with an informed prior – … Continue reading
Posted in Bayes, Data Driven Management, Probability, PYMC, Statistics
Tagged Bayes, Probability, PYMC, Statistics
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Why (sample) size matters – Bayesian Inference
[The following example is adapted from Richard McElreath’s excellent “Statistical Rethinking”] Let’s say you have bought a bag of marbles. The marbles in the bag are either blue or white. But you don’t know the proportions, that is, the ratio … Continue reading
Posted in Bayes, Probability, PYMC, Python, Statistics
Tagged Bayes, Probability, PYMC, Python, Statistics
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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 Bayes, Gambling, Machine Learning, Matplotlib, Numpy, Pandas, Probability, PYMC, Python, Scipy, Simulation, sports, Statistics, Technology
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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 Bayes, Data Analysis, Matplotlib, Numpy, Pandas, Probability, PYMC, Python, Scipy, Statistics, Val2018
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Val2018 – samband mellan röstning och inkomst/utbildningsnivå
Det börjar bli många olika analyser av valresultatet på den här bloggen, så här kommer ytterligare en: En regressionsanalys över valdatat (från Valmyndigheten) och befolkningsdatat (från SCB): Bayesian Linear Regression över sambanden Röstandelar per parti vs andelen högutbildade (minst 3 … Continue reading
Posted in Bayes, Data Analytics, Politik, Probability, PYMC, Python, Society, Statistics, Sverige
Tagged Bayes, Pandas, Politik, Probability, PYMC, Python, Statistics, Sverige, Val2018
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PYMC – Markov Chain Monte Carlo regression – canonical example
Posted in Bayes, Data Analytics, Numpy, Probability, PYMC, Python, Statistics
Tagged Bayes, Markov Chain Monte Carlo, Matplotlib, Numpy, Probability, PYMC, Python, Scipy, Statistics
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Val2018 – röstningsmönster i Sveriges län och Stockholms kommuner
I tidigare inlägg har jag redogjort för hur min Bayesian Inference valprediktion lyckades (riktigt bra, tack för att du frågar, bättre än många proffs-tyckare, faktiskt!) 🙂 I detta inlägg presenteras några obearbetade “rådata” kring valutgången och populationen i dels samtliga … Continue reading
Posted in Bayes, Data Analytics, Numpy, Politik, Probability, PYMC, Python, Society, Statistics, Sverige
Tagged Bayes, Politik, Probability, Statistics, Sverige, Val2018
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