# Dice Roller

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Dice Roller is a tool that simulates the rolling of dice. It is used in various games and applications that require random number generation. The tool is designed to provide a fair and unbiased result, and it is commonly used in board games, role-playing games, and gambling.

## Concepts

Dice Roller is based on the concept of probability. Probability is the measure of the likelihood of an event occurring. In the case of Dice Roller, the probability of rolling a particular number on a die is equal to the reciprocal of the number of sides on the die. For example, if you roll a six-sided die, the probability of rolling a one is 1/6.

## Formulae

The formula for calculating the probability of rolling a particular number on a die is:

P = 1/n

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Where P is the probability of rolling the number, and n is the number of sides on the die.

## Benefits

Dice Roller provides several benefits, including:

• Fairness: Dice Roller provides a fair and unbiased result, making it ideal for use in games and applications that require random number generation.
• Convenience: Dice Roller is easy to use and can be accessed from anywhere with an internet connection.
• Customization: Dice Roller can be customized to simulate different types of dice, including those with different numbers of sides.
• Efficiency: Dice Roller can generate multiple random numbers quickly and efficiently, making it ideal for use in games and applications that require a large number of random numbers.

## Interesting Facts

• The oldest known dice were found in Iran and date back to around 2800 BCEย 1.
• Dice were used in ancient Rome for gambling and entertainmentย 1.
• The probability of rolling a seven on two six-sided dice is 1/6ย 1.
• The probability of rolling a twelve on two six-sided dice is 1/36ย 1.

## References

Here are some scholarly references that you may find useful:

• Sicart, M. (2008). Defining game mechanics.ย Game Studies, 8(2)1
• Saad, F. A., Freer, C. E., Rinard, M. C., & Mansinghka, V. K. (2020). The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions.ย arXiv preprint arXiv:2003.038302
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