The way to change the world…
... is getting people to care !
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
"Without deviations from the norm, progress is not possible" -- Frank Zappa
"I must study politics and war that my sons may have liberty to study mathematics and philosophy and they in turn must study those subjects so that their children can study painting, poetry, music, architecture, statuary, tapestry, and porcelain." -- John Adams, Americas II president
"In a time of universal deceit - telling the truth is a revolutionary act." -- George Orwell
"The only thing necessary for the triumph of evil is for good men to do nothing." -- Edmund Burke
"Being normal is being merely average" -- Shawn Achor
“It is difficult to get a man to understand something, when his salary depends on his not understanding it.” -- Upton Sinclair
"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
- agile
- AI
- algorithm
- Architecture
- Artificial Intelligence
- Bayes
- Big Data
- Big Government
- Black Swan
- Brottsstatistik
- business
- Clayton Christensen
- complex systems
- Culture
- Data Analysis
- development
- Dynamic Non-linear systems
- Empty Suit
- environment
- Eurostat
- Feature Centric Development
- FFT
- Functional Stupidity
- Gambling
- genetic
- Gephi
- global finance
- Grandiosity
- HOCKEY-2018
- image processing
- innovation
- leadership
- Machine Learning
- management
- Math
- Matplotlib
- metrics
- Monte Carlo
- Navigation
- Neural networks
- Numpy
- Optimization
- organization
- Pandas
- performance
- Politics
- Politik
- Probability
- programming
- PYMC
- Pystan
- Python
- Radical management
- Research
- Samhälle
- Science
- Scipy
- SelfOptimizingAgile
- Simulation
- SNA
- Society
- software
- sports
- STAN
- Statistics
- Steve Denning
- Surveillance
- Sverige
- systems
- Taleb
- Technical Debt
- Technology
- Telecom
- Val2018
Follow me on Twitter
My Tweets-
Recent Posts
Top Posts & Pages past few days
- Fast Fourier Transforms with Python - the noise and the signal
- More fun with Python: Navigational tricks - AIS NMEA encoding/decoding
- The blindfolded archer - Cauchy distribution: when randomness gets wild
- Parsing NMEA 0183 sentences in Python
- Python - plotting with different scales
- Image processing with Python, Numpy & Scipy - image convolution
- New Cxx level officer needed : CVO - Chief Value Officer
- Making opencv work on WIndows7 Cygwin 64-bit : 7 hour's of painful trial & error
- Wind,Temperature,Air Pressure statistics
- Linear Regression - comparing Bayesian with Frequentist approach
Archives
- May 2019
- February 2019
- January 2019
- December 2018
- October 2018
- September 2018
- August 2018
- July 2018
- June 2018
- May 2018
- April 2018
- March 2018
- February 2018
- January 2018
- December 2017
- November 2017
- October 2017
- September 2017
- August 2017
- October 2014
- September 2014
- June 2014
- May 2014
- April 2014
- March 2014
- February 2014
- January 2014
- December 2013
- November 2013
- October 2013
- September 2013
- August 2013
- July 2013
- June 2013
- May 2013
- April 2013
- February 2013
- January 2013
- December 2012
- November 2012
- October 2012
- September 2012
- August 2012
- July 2012
- June 2012
- May 2012
- April 2012
- March 2012
Blogroll
Top Clicks
Categories
- A/B Testing
- Agile
- AI
- API
- Bayes
- Behavioral Economics
- Big Business
- Big Data
- Big Government
- Business
- Climate
- Complex Systems
- Culture
- Data Analytics
- Data Driven Management
- development
- FIFA
- Finance
- Functional Stupidity
- Gambling
- Grandiosity
- HOCKEY-2018
- Innovation
- Kanban
- KERAS
- Leadership
- Lean
- Machine Learning
- Management
- Maritime Technology
- Math
- Nautical Information Systems
- Neural networks
- NMEA
- Non Linear Dynamic Systems
- Numpy
- Organization
- Pandas
- performance
- Politik
- Probability
- PYMC
- Pystan
- Python
- Research
- SelfOptimizingAgile
- Simulation
- SNA
- Society
- software
- sports
- STAN
- Statistics
- Surveillance
- Sverige
- Systems
- TCPIP
- Technology
- Telecom
- Uncategorized
- Web
Meta
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
Leave a comment
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
Leave a comment
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
Leave a comment
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
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 Bayes, Probability, PYMC, Python, Statistics
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 Bayes, Gambling, Machine Learning, Matplotlib, Numpy, Pandas, Probability, PYMC, Python, Scipy, Simulation, sports, Statistics, Technology
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 Bayes, Data Analysis, Matplotlib, Numpy, Pandas, Probability, PYMC, Python, Scipy, Statistics, Val2018
1 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 Bayes, Pandas, Politik, Probability, PYMC, Python, Statistics, Sverige, Val2018
Leave a comment
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
Leave a comment
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
Leave a comment
