Category Archives: Statistics

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 , , , | Leave a comment

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 , , , , | Leave a comment

Global Warming – soon in a place near you

Just downloaded hourly temperature data from SMHI, the Swedish Meteorological & Hydrological Institute, taken on Svenska Högarna, one of the islands in the remote Stockholm Archipelago, for the years 1949 – 2018. The plots below present max/min/mean daily temps for … Continue reading

Posted in Big Data, Data Analytics, Statistics | 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

Val2018 – top50 & bottom50 kommuner per parti

“Jag vill ha mer val Ge mig mer val Jag vill ha mer val Ge mig mer val Tusen stjärnor som tindrar Glitter så långt jag ser Av valljus som glimmar Vill jag ha mer..” Var det inte så de … Continue reading

Posted in Data Analytics, Politik, Statistics, Sverige | Tagged , , , | Leave a comment

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 , , , , , , , , | Leave a comment

Val2018 – top50 & bottom50 samtliga valdistrikt i Sverige för samtliga partier

Som komplement till de två tidigare inläggen [1,2] som redogjorde för valdistrikten i Stockholms kommun, så kommer här top50 & bottom50 för samtliga de 6004 valdistrikt som finns med i valnattens preliminära resultat. Top-50: Bottom-50:

Posted in Data Analytics, Politik, Society, Statistics, Sverige | Tagged , , , , | Leave a comment

Val2018 – Partiernas sämsta valdistrikt inom Stockholms kommun

Förra inlägget visade partiernas top-50, här visas partiernas bottom-50.

Posted in Data Analytics, Society, Statistics | Tagged , , , | Leave a comment

PYMC – Markov Chain Monte Carlo regression – canonical example

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