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# System prepended metadata

title: Course Overview
tags: [inference]

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tags: inference
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Course Overview
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This course is the first of a two-course sequence on Advanced Inference, which essentially refers to asymptotic statistical theory. In this course we will cover the development of the past many decades pertaining to parametric inference, and in the second course non-parametric inference will be covered. Some probabilistic tools that are common to both parts will also be covered in this course. 

Caveat
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A strong background in real analysis, comfort with working with complex numbers, background in metric space topology, and some basic familiarity with tools of measure theoretic probability theory will be assumed. A deficiency in any of these areas can be overcome with a liking for abstract math, and high levels of motivation and perseverance - these also are, by the way, by themselves other pre-requisites for this course. 


## References

- [My favorite Measure Theoretic Probability Theory Book](https://services.math.duke.edu/~rtd/PTE/PTEv5a.pdf)
    - Good things in life are often available for no cost
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## Status of Notes

1. This will be the third time I am teaching this course, and these notes have benefitted from the efforts of past students and TAs. 
2. If you find any typos or errors please let me or the TA aware of it. 

