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# Statistical Thinking using Randomisation and Simulation
## Statistical distributions
### <NAME> (<a href="mailto:<EMAIL>"><EMAIL></a>, <span class="citation">@visnut</span>)
### W3.C1

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# Overview of this class

- Random numbers
- Mapping random numbers to events for simulation
- Statistical distributions
- Density functions
- READING: [Data Scientist’s Crib Sheet](https://blog.cloudera.com/blog/2015/12/common-probability-distributions-the-data-scientists-crib-sheet/), [Gallery of distributions](http://www.itl.nist.gov/div898/handbook/eda/section3/eda366.htm), [Distributions for Actuaries](http://www.casact.org/pubs/monographs/papers/02-Bahnemann.pdf)

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# Random numbers

- True random number generators: [Radioactive decay](https://www.fourmilab.ch/hotbits/), [electromagnetic field of a vacuum](https://qrng.anu.edu.au)
- Computers only technically provide pseudo-random numbers, using deterministic process, e.g linear congruential, for large `\(a, b, m\)`

`$$X_{n+1} = (aX_n + b) ~~mod ~~m$$`<commit_msg>reading removed<commit_after><!DOCTYPE html>
<html>
  <head>
    <title>Statistical Thinking using Randomisation and Simulation</title>
    <meta charset="utf-8">
    <meta name="author" content="<NAME> (<EMAIL>, @visnut)" />
    <link href="week3.class1_files/remark-css-0.0.1/example.css" rel="stylesheet" />
    <link rel="stylesheet" href="myremark.css" type="text/css" />
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class: center, middle, inverse, title-slide

# Statistical Thinking using Randomisation and Simulation
## Statistical distributions
### <NAME> (<a href="mailto:<EMAIL>"><EMAIL></a>, <span class="citation">@visnut</span>)
### W3.C1

---




# Overview of this class

- Random numbers
- Mapping random numbers to events for simulation
- Statistical distributions
- Density functions


---
# Random numbers

- True random number generators: [Radioactive decay](https://www.fourmilab.ch/hotbits/), [electromagnetic field of a vacuum](https://qrng.anu.edu.au)
- Computers only technically provide pseudo-random numbers, using deterministic process, e.g linear congruential, for large `\(a, b, m\)`

`$$X_{n+1} = (aX_n + b) ~~mod ~~m$$`
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{'dir': 'git-commits-cleaned', 'id': '5916844', 'n_tokens_mistral': 909, 'n_tokens_neox': 836, 'n_words': 209}