Bayesian methods for hackers pdf download

Need an account? Click here to sign up. Download Free PDF. Chelsea Sims. A short summary of this paper. Condition: New. Language: English. Brand new Book. The below chapters are rendered via the nbviewer at nbviewer. Prologue: Why we do it. Chapter 1: Introduction to Bayesian Methods Introduction to the philosophy and practice of Bayesian methods and answering the question, "What is probabilistic programming?

How do we create Bayesian models? Chapter 5: Would you rather lose an arm or a leg? The introduction of loss functions and their awesome use in Bayesian methods. Chapter 6: Getting our prior -ities straight Probably the most important chapter.

We examine our prior choices and draw on expert opinions craft priors. More questions about PyMC? Please post your modeling, convergence, or any other PyMC question on cross-validated , the statistics stack-exchange. Below are just some examples from Bayesian Methods for Hackers. Inferring behaviour changes using SMS message rates Chapter 1.

By only visually inspecting a noisy stream of daily SMS message rates, it can be difficult to detect a sudden change in the users's SMS behaviour. In our first probabilistic programming example, we solve the problem by setting up a simple model to detect probable points where the user's behaviour changed, and examine pre and post behaviour.

AB testing, also called randomized experiments in other literature, is a great framework for determining the difference between competing alternatives, with applications to web designs, drug treatments, advertising, plus much more. With our new interpretation of probability, a more intuitive method of AB testing is demonstrated. And since we are not dealing with confusing ideas like p-values or Z-scores, we can compute more understandable quantities about our uncertainty.

A very simple algorithm can be used to infer proportions of cheaters, while also maintaining the privacy of the population. For each participant in the study: Have the user privately flip a coin. If heads, answer "Did you cheat? If tails, flip again.

If heads, answer "Yes" regardless of the truth; if tails, answer "No". This way, the suveyor's do not know whether a cheating confession is a result of cheating or a heads on the second coin flip. But how do we cut through this scheme and perform inference on the true proportion of cheaters? On January 28, , the twenty-fifth flight of the U. The presidential commission on the accident concluded that it was caused by the failure of an O-ring in a field joint on the rocket booster, and that this failure was due to a faulty design that made the O-ring unacceptably sensitive to a number of factors including outside temperature.

Of the previous 24 flights, data were available on failures of O-rings on 23, one was lost at sea , and these data were discussed on the evening preceding the Challenger launch, but unfortunately only the data corresponding to the 7 flights on which there was a damage incident were considered important and these were thought to show no obvious trend.

We examine this data in a Bayesian framework and show strong support that a faulty O-ring, caused by low abmient temperatures, was likely the cause of the disaster. The prior-posterior paradigm is visualized to make understanding the MCMC algorithm more clear. For example, below we show how two different priors can result in two different posteriors. Given a dataset, sometimes we wish to ask whether there may be more than one hidden source that created it.

A priori , it is not always clear this is the case. We introduce a simple model to try to pry data apart into two clusters. Consider ratings on online products: how often do you trust an average 5-star rating if there is only 1 reviewer?

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