Probability distributions within reliability are Python objects, which allows us to specify just the type of distribution and its parameters. Once the distribution object is created, we can access a large number of methods (such as PDF() or plot()).

In this example we will look at how to create a probability plot that has different colors representing different groups which are being analysed together.

""" Probability plotting This module contains the functions required to generate linearized probability plots of the 8 standard distributions included in reliability. The most common use of these type of probability plots is to assess goodness of fit.

The Python library reliability bridges the gap between traditional GUI based programs and pure code. The functions within the library provide reliability engineers with a suite of tools that can perform analysis equivalent to most GUI based software tools.

reliability is a Python library for reliability engineering and survival analysis. It significantly extends the functionality of scipy.stats and also includes many specialist tools that are otherwise only available in proprietary software.

Reliability diagrams are useful in displaying the conditional bias of probabilistic forecasts. For more information about reliability diagrams, refer to the METviewer documentation. 10.2. Example. 10.2.1. Sample Data. The data is text output from MET in columnar format.

reliability is a Python library for reliability engineering and survival analysis. It significantly extends the functionality of scipy.stats and also includes many specialist tools that are otherwise only available in proprietary software. Detailed documentation and examples are available at readthedocs. Easy creation of distribution objects.

Jun 1, 2023 · A reliability diagram is a diagnostic plot to understand the quality of a probabilistic forecast for categorical events. An example of a categorical event can be the probability for the daily average temperature in Paris to be in the lower tercile of the climatological distribution.

There are at least 2 very common ways to show a reliability diagram for a probabilistic binary classifier. Drawing a line between all the binned mean predictions and the true proportion of positives. And showing bars instead of a lineplot, usually with errorbars showing the discrepancy with respect to a perfectly calibrated model (diagonal)

This section contains two different styles of probability-probability (PP) plots. These are the fully parametric probability-probability plot ( reliability.Probability_plotting.PP_plot_parametric ) and the semi-parametric probability-probability plot ( reliability.Probability_plotting.PP_plot_semiparametric ).