Category Archives: Pandas

More Mindbending Probability

In a previous post, I discussed the seemingly unintuitive logic of the famous Monty Hall Problem. However, with some careful thinking, even without resorting to Monte Carlo Simulation, I’m able to make sense of that apparent paradox. However, the paradox … Continue reading

Posted in Math, Numpy, Pandas, Probability, Python | Tagged , | 2 Comments

Covid Sweden – status 2020-08-03

SCB.se just released their weekly preliminary stat’s on deaths all causes. However, before looking at that data, let’s take a look at how Covid has evolved in Sweden, based on the numbers from Johns Hopkins University: Something remarkable seems to … Continue reading

Posted in Bayes, Data Analytics, Epidemics, MCMC, Pandas, Probability, PYMC | Tagged , , , , , | Leave a comment

Covid-19 Sweden: A summary

Sweden has been painted as the Black Sheep of the international community with respect to its ways to deal with the Covid-19 virus.  People and orginizations around the world were very quick to paint Sweden as the Sodom & Gomorra … Continue reading

Posted in Bayes, Data Analytics, Epidemics, MCMC, Pandas, Politik, Probability, PYMC, Python, Society, Statistics, Sverige | Tagged , , , , , , , , , , , | 4 Comments

Sweden : Age adjusted deaths 2000-2020

Population demographics have a significant impact on the number of deaths in a society. Among the many demographic factors that impact number of deaths is age. Other confounding factors (which will not be dealt with in this post) include general … Continue reading

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Corona Sweden : Infection Fatality Rate

It’s been a while ago since I ran my MCMC-hack to estimate the Swedish Infection Fatality Rate, main reason being that each run takes an awful lot of computing time, around 10h, to get the MCMC to converge… and during … Continue reading

Posted in Bayes, Data Analytics, Epidemics, MCMC, Pandas, Probability, PYMC, Python, Statistics | Tagged , , , , , , , , , | Leave a comment

Fooled by Averages and Ignorant of Uncertainty – Bayesian Inference to assistance

As a follow up to my previous posts [1,2,3] on the danger’s of relying upon averages, and Simpson’s paradox, which is a consequence of misused averaging, here’s yet another angle on the same topic. Let’s first resume with the baseball … Continue reading

Posted in Bayes, Data Analytics, Gambling, Pandas, Probability, PYMC, Python, Statistics | Tagged , , , , , , | 3 Comments

Corona – What’s the true Mortality Rate, and how many infected are there, anyways…? [in all likelihood, some good news!]

[Note: this is a somewhat technical / mathematical article, so those of you who only want the daily Corona updates, “the current numbers”, might want to skip this one] As we all probably know by now, the official numbers on … Continue reading

Posted in Bayes, Data Analytics, Epidemics, Finance, Pandas, Probability, Pystan, Python, Research, Simulation, Society, Statistics | Tagged , , , , , , , | Leave a comment

Corona Update 2020-03-16

Data up until yesterday: Sweden: growth factor decreased marginally over the past few days, but that’s most likely due to the fact that since a couple of days back, general testing is not done; only those in critical condition will … Continue reading

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Corona Update 2020-03-15

Data up until yesterday: Sweden still on track to 10000 infected by 20th March, and 100.000 a week after that. At global level, the virus is now growing fast again, after having slowed down considerably in mid-feb. Mean growth factor … Continue reading

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Corona update 2020-03-14

Data uptil yesterday, except for first plot, which also contains today’s data: unfortunately, it looks like my “gutsy” prediction from yesterday, that Sweden will have 10.000 infected in a week from now, still stands. And if the trend continues for … Continue reading

Posted in Bayes, Data Analytics, Epidemics, Organization, Pandas, Probability, PYMC, Research, Society, Statistics, Sverige | Tagged , , , , , | Leave a comment