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Books & Free Class

Class & code 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.” Update: 2017 was just as strong.

Note: the more attractive cover image was a gift from Wrath of Gnon.

THE BIG GIST

  1. All probability is conditional;
  2. Probability is not decision.

From those simple and proved truths flow 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.

REVIEWS

The New Criterion Mathematical Association of America
Don Aitkin Thorsten Jorgen Ottosen
The Philosopher Vox Popoli
Journal of American Physicians and Surgeons Amerika

RELEVANT ARTICLES

  • Judea Pearl Is Wrong On AI Identifying Causality, But Right That AI Is Nothing But Curve Fitting (link)
  • Proof Cause Is In The Mind And Not In The Data (link)
  • 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)
  • Quantum Potency & Probability (link)
  • Is Presuming Innocence A Bayesian Prior? (link)
  • There Is No “Problem” Of Old Evidence In Bayesian Theory (link)
  • There Is No Prior? What’s A Bayesian To Do? Relax, There’s No Model, Either (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)
  • Everything Is Already In The Simulation (link)
  • Making Random Draws Is Nuts (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)

CLASSES

  1. How To Do Predictive Statistics: Part I: Introduction: MUST READ (link)
  2. How To Do Predictive Statistics: Part II: Regression 1 (link)
  3. How To Do Predictive Statistics: Part III: Regression 2 (link)
  4. How To Do Predictive Statistics: Part IV: Logistic Regression (link)
  5. How To Do Predictive Statistics: Part V: Multinomial Regression (link)
  6. How To Do Predictive Statistics: Part VI: Poisson Regression (link)
  7. How To Do Predictive Statistics: Part VII: Tobit Regression (link)
  8. New! How To Do Predictive Statistics: Part VIII: Starting Stan regression (link)
  9. New! How To Do Predictive Statistics: Part IX: Logistic & Beta Regression (link)
  1. Choose Predictive Over Parametric Every Time (link)
  2. The Solution To The Doomsday Argument (link)
  3. Falsifiability Is Falsifiable (link)
  4. A Beats B Beats C Beats A (link)
  5. Quantum Potency & Probability (link)
  6. Against Moldbug’s Reservationist Epistemology: Reason Alone Is Not Reasonable (link)

Free Software!: mcmc.pred.R, mcmc.pred.examples.R. Data are linked in individual posts.

Applied Data Science/Statistics/Applied Probability. Free on-line class general link.

OTHER BOOKS

Breaking the Law of Averages, a simple introduction to some of the main ideas of Uncertainty; really notes for an introductory class. Free PDF or buy the hardcover.

So, You Think You’re Psychic?, a skeptical framework for testing for would-be psychics. Free PDF or buy the hardcover. This was written long before I figured out the true meaning of probability.

8 thoughts on “Books & Free Class Leave a comment

  1. Perhaps these are overly picky things to consider:
    Uncertainty,
    1) pg 3 , 3rd paragraph, consider change “… insist all triangles have … ” to “… insist all planar triangles have … ”
    2) of 4, 4th paragraph change “I don’t consider idealism to be on any interest.” to “I don’t consider idealism to be of any interest.”

  2. I’ll probably buy the book anyway and not wait for a paperback version (if any). Is it ok to post feedback, comments, questions, typos, here?

  3. Page 10 first paragraph under “Science and Scientism” should be

    “That radium *has* the atomic weight of w might be false…”

    and not

    “That radium does not have the atomic weight of w might be false…”

  4. Dear Dr Briggs,

    I am enjoying your book. There is so much I philosophically agree with it and will feature it on my blog. I find your book timely. I have encountered quite a number of scientists who have no philosophical appreciation at all, some are quite allergic to it.

    I am so glad you wrote it.

    LPC

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