The quickest and easiest way to calculate the average level of information with the help of this software that helps you determine the concept called the Shannon Entropy

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Named after the famous Claude Shannon, often regarded as “the father of information theory,” the Shannon Entropy is a concept that allows one to measure the uncertainty (or information, or surprise) level of a random process.

Calculate the Shannon Entropy in mere seconds

As its name clearly states,ย Shannon Entropy Calculatorย is a simple, Windows-only application developed to help you calculate the entropy that each event contributes with (uncertainty per even), and the total entropy from all give events (total number of uncertainty bits). The results are measured in bits of uncertainty.

Please note that the sum of all probabilities cannot be higher than 1 ( meaning that their sum must be 1). Other than that, it’s worth noting that .NET Framework 4.7.2 or higher must be installed on your computer to be able to make the app work.

Extremely simplistic application

The app is as simple as they come with a straightforward GUI and nothing like menus and extra features. It can be run on all machines running Windows 8 or newer, and it can be used by pretty anyone quite easily.

It’s a portable application, meaning that it does not require installation. You just have to download the archive, open it, and run the app. From this point onwards, simply input the probability events (Events numbered from 1 to 10), and hit the “Calculate” button. The results will be displayed almost instantly on the right side of the app’s GUI.

Helps you determine the uncertainty levels of any random process

To conclude, Shannon Entropy Calculator is a simple yet effective software that allows just about anyone to effortlessly calculate the Shannon Entropy for a set number of given events.ย  It’s easy to set up, even easier to use, and it delivers results in mere seconds.

We at AppsForMyPC have tested and used Shannon Entropy Calculator and it worked pretty smoothly ๐Ÿ™‚ ๐Ÿ™‚