CSC 140 Lecture Notes - Lecture 11: Pseudorandom Number Generator, Rand Corporation, Numerical Analysis
3 Jun 2020
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Document Summary
Random numbers: application of random numbers, simulation, simulate natural phenomena, sampling. It is often impractical to examine all possible cases, but a random sample will provide insight into what constitutes typical behavior: numerical analysis, computer programming, decision making, recreation. Two types of random numbers: true random numbers are generated in nondeterministic ways. They are not repeatable: pseudorandom numbers are numbers that appear random, but are obtained in a deterministic, repeatable, and predictable manner. True random generators: use one of several sources of randomness, decay times of radioactive material, electrical noise from a resistor or semiconductor, radio channel or audible noise, keyboard timings, some are better than others, usually slower than prngs. Natural random number: no two snowflakes are the same, sources, white noise, water molecule distribution, generation, measurement, irreproducible, errors. It is not viable to generate a true random number using computers since they are deterministic. Independence: reproducibility, portability, efficiency, a sufficiently long period.
