用GameLoop模拟器在電腦上玩Random Number Generator

Random Number Generator 電腦版

來自發行商 Luckywhiteapps 的 Random Number Generator 能夠在安卓系統上運行。

現在，您可以在電腦上使用 GameLoop 模擬器流暢地玩 Random Number Generator。

在 GameLoop 遊戲庫或搜索結果中下載。 不需要再暢玩遊戲時盯著電池電量或被令人懊惱的來電打斷。

只需在電腦大屏幕上免費下載暢玩Random Number Generator 電腦版！

Random Number Generator 簡介

With this random number generator free app you can easily generate random numbers.

What is random number?

Random numbers are numbers that occur in a sequence such that two conditions are met: (1) the values are uniformly distributed over a defined interval or set, and (2) it is impossible to predict future values based on past or present ones. Random numbers are important in statistical analysis and probability theory.

The most common set from which random numbers are derived is the set of single-digit decimal numbers {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}. The task of generating random digits from this set is not trivial. A common scheme is the selection (by means of a mechanical escape hatch that lets one ball out at a time) of numbered ping-pong balls from a set of 10, one bearing each digit, as the balls are blown about in a container by forced-air jets. This method is popular in lotteries. After each number is selected, the ball with that number is returned to the set, the balls are allowed to blow around for a minute or two, and then another ball is allowed to escape.

How Computers Generates Rando Numbers?

Pseudorandom numbers are an alternative to “true” random numbers. A computer could use a seed value and an algorithm to generate numbers that appear to be random, but that are in fact predictable. The computer doesn’t gather any random data from the environment.

This isn’t necessarily a bad thing in every situation. For example, if you’re playing a video game, it doesn’t really matter whether the events that occur in that game are cased by “true” random numbers or pseudorandom numbers. On the other hand, if you’re using encryption, you don’t want to use pseudorandom numbers that an attacker could guess.

For example, let’s say an attacker knows the algorithm and seed value a pseudorandom number generator uses. And let’s say an encryption algorithm gets a pseudorandom number from this algorithm and uses it to generate an encryption key without adding any additional randomness. If an attacker knows enough, they could work backwards and determine the pseudorandom number the encryption algorithm must have chosen in that case, breaking the encryption.