Class links are at the bottom.
This is the permanent page for Uncertainty: The Soul of Modeling, Probability & Statistics. E-copies can be had from Springer in EPUB and PDF formats. Amazon has a Kindle version (but I’ve heard bad things about its formatting).
Important student note: apparently many universities have a deal with Springer such that you can read for free the book on your university’s library site.
Uncertainty’s a hit! 821 paper copies sold and 7,915 downloads: “This means your book was one of the top 25% most downloaded eBooks in the relevant
SpringerLink eBook Collection in 2016.”
Note: the more attractive cover image was a gift from Wrath of Gnon.
THE BIG GIST
- All probability is conditional;
- Probability is not decision.
From those simple and proved truths flows these consequences:
- Probability cannot discern cause;
- Therefore no hypothesis test, by wee p-value or Bayes factor, should ever be used;
- Parameters are of no interest to man or beast;
- Only verified probability models should be used and in a predictive sense;
- To understand cause and provide explanation we must look to nature, essence, and power.
Read the book and be the first on your block to come to a wondrous, penetrating understanding of probability & statistics. Out with the new and in with the old! The older, better, and true understanding of cause and probability, that is. Eschew mathematics for the sake of mathematics, flee ad hocery in all its forms and wiles, and put probability to its intended real use!
This includes you, too, computer scientists, with your big deep data neural net machine “learning” fuzzy algorithms, which are all probability models with (admittedly) better names.
|The New Criterion||Mathematical Association of America|
|Don Aitkin||Thorsten Jorgen Ottosen|
|The Philosopher||Vox Popoli||Journal of American Physicians and Surgeons||Amerika|
- Parameters Aren’t What You Think (Here’s What They Are) (link)
- JASA: The Substitute for P-Values (link)
- Manipulating the Alpha Level Cannot Cure Significance Testing (link)
- There Is No “Problem” Of Old Evidence In Bayesian Theory (link)
- How To Resolve All Probability Paradoxes: Apples In Sack Example (link)
- P-values vs. Bayes Is A False Dichotomy (link)
- Signal + Noise vs. Signal (link)
- What Neural Nets Really Are (link)
- Every Result Of Unsupervised Learning Is Correct; Or, All Learning Is Supervised (link)
- The Gremlins Of MCMC: Or, Computer Simulations Are Not What You Think (link)
- The Hierarchy Of Models: From Causal (Best) To Statistical (Worst) (link)
- The Solution To The Doomsday Argument (link)
- Real Versus Statistical Control (link)
- Formal Logic And Probability (link)
- Bayesian Statistics Isn’t What You Think (link)
- Falsifiability Is Not That Useful (link)
- The Difference Between Essential And Empirical Models (link)
- Under-determination, Quus, And Why It’s Cause That Counts (And With A Taste Of Grue) (link)