- #1

WMDhamnekar

MHB

- 376

- 28

Hi,

Would any member of Math Help Board explain me the highlighted area in the following paragraphs?

Now the only missing thing in previous cases is how would one generate a Uniform random, Normal random distributions. We therefore look to cover algorithms to generate such Uniform random distributions and also methods to transform these to other distributions such as Normal Distributions.

The numbers that we will be generating in

Uniform Distribution is a continuous distribution abbreviated as

The most popular method for generating such numbers is the

Thus if we choose a x₀ also called the

If the above conditions for non-zero

Would any member of Math Help Board explain me the highlighted area in the following paragraphs?

**Generating Random Distributions**Now the only missing thing in previous cases is how would one generate a Uniform random, Normal random distributions. We therefore look to cover algorithms to generate such Uniform random distributions and also methods to transform these to other distributions such as Normal Distributions.

The numbers that we will be generating in

**for these simulations are actually called***R***numbers which are not truly random but pretty good at mimicking genuine randomness. We generate them using deterministic algorithms such that they approximate the properties of sequence of random numbers.***pseudorandom***Uniform Distribution**Uniform Distribution is a continuous distribution abbreviated as

**Where the***U(a,b).***probability density function**is**Linear Congruential Generators**. The LCG is generated using-Thus if we choose a x₀ also called the

**seed**. We can generate**m-1**unique Uniform Random Numbers between**[0,1]**. Though a good estimator must follow certain conditions for non zero**c**.If the above conditions for non-zero

**c**are not fulfilled by a good estimator, what are its effects and results?
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