Posts tagged censored

Reparameterizing the Weibull Accelerated Failure Time Model

This notebook uses libraries that are not PyMC dependencies and therefore need to be installed specifically to run this notebook. Open the dropdown below for extra guidance.

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Bayesian Survival Analysis

Survival analysis studies the distribution of the time to an event. Its applications span many fields across medicine, biology, engineering, and social science. This tutorial shows how to fit and analyze a Bayesian survival model in Python using PyMC.

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Reliability Statistics and Predictive Calibration

Duplicate implicit target name: “reliability statistics and predictive calibration”.

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Bayesian regression with truncated or censored data

The notebook provides an example of how to conduct linear regression when your outcome variable is either censored or truncated.

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Censored Data Models

This example notebook on Bayesian survival analysis touches on the point of censored data. Censoring is a form of missing-data problem, in which observations greater than a certain threshold are clipped down to that threshold, or observations less than a certain threshold are clipped up to that threshold, or both. These are called right, left and interval censoring, respectively. In this example notebook we consider interval censoring.

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