The Naked Future by Patrick Tucker Read Free Book Online

Pearl describes the process as follows: the Bayesian interpretation of probability is one in which we “encode degrees of belief about events in the world, and data are used to strengthen, update or weaken those beliefs.” 17
    Compared with many other statistical methods such as traditional linear regression, Bayes is one of the most like the brain. Predictions of probability combine past experience with sensed input to create a (somewhat) moving picture of the future.
    What’s important to understand is that although Thomas Bayes’s formula wasn’t published until 1764, about three years after his death, it’s only in the last couple of decades that Bayes has come to be seen as the essential lens through which to understand probability in a wide number of contexts. The Bayesian formula plays a critical role in statistical research methods having to deal with computer and AI problems but also the simple questions of quantifying what may happen.
    When I asked the researchers in this book why they foundBayes more useful than other statistical methods for their work, the most common response I received was that Bayesian inference allows you to update a probability assumption—the degree of faith you have in a particular outcome—on the basis of new information, new details, and new facts, and to do so very quickly. If the interconnected age has taught us nothing else, it is that there will
always
be new facts. Bayes lets you speedily move closer to a better answer on the basis of new information.
    Here’s an example. Let’s say it’s Tuesday and you are scheduled to meet your therapist. Your therapist has never missed a Tuesday appointment so you hypothesize that the probability of her showing up is 100 percent. The
P
for
A
or
P(A)
= 1. This is your
prior
belief. Obviously, it’s terrible. There is never a 100 percent chance that someone will show up to work. Now, let’s say you get some new information, that your therapist has just left from a previous appointment and she is three miles away, on foot. How would you go about adjusting your belief to more accurately reflect the probability that your therapist will make it to your appointment on time?
    Let’s say you find some new data, that the average walking speed is 3.1 miles per hour. Given time and distance you can compute that your therapist will surely be late. But you must compute this in light of the prior value; your therapist is
never
late. You now know the chances of your therapist being late for this appointment are lower than they would be for a regular person but the possibility of her being late for your appointment, in spite of what you understand to be the lessons of all history, have grown significantly. Now you discover even more information: according to reviews of your therapist’s practice on Yelp, she’s actually late to her appointments about half the time. You can recompute the probability of your therapist’s getting to the appointment on time over and over, every time you get some new tidbit that reveals reality more clearly in all its inconvenience. What is making the future more transparent is the exponentially growing number of tidbits we have to work with. Bayes lets us manage that growth.
    Imagine next that you have an enormous amount of telemetrically gathered information to update your prior assumption. You can actually track your therapist moving toward you in real time through her Nike+ profile. You can read the wind currents meeting her via Cosm’s feed off a nearby wind sensor. You can measure her heart rate and hundreds of other signals that might further refine your understanding of where she is going to be, relative to you, in the next few minutes. Let’s say you also have access to an enormous supercomputer capable of running thousands of simulations a minute, enabling you to weigh and average each new variable and piece of information more accurately. The