eggplantbren/student-resources.html

## student-resources.html
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    <title>Resources for research students</title>
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<body>
    <div class="content">

    <!-- Welcome paragraph -->
    <h1>Welcome</h1>
    <p>
        Here, I'll collect links to resources for learning various tools
        and methods, some of which will be useful for research students working
        with me. I'll generally only recommend things I've used or read myself.
        Some of these are online resources, others are books that may or may
        not be available at the Auckland uni library or at Auckland libraries
        in general.
        You are encouraged to search for other ones as well!
    </p>

    <p>
        <a href="#bayes">Bayesian Inference</a><br>
        <a href="#nestedsampling">Nested Sampling</a><br>
        <a href="#dns">Diffusive Nested Sampling</a><br>
        <a href="#computing">Computing</a>
    </p>

    <!-- Resources for Bayesian inference -->
    <a name="bayes"></a><h2>Bayesian inference</h2>

    <p class="small">
        <b>Data Analysis: A Bayesian Tutorial</b> by Sivia and Skilling.
        Good for beginners with a physics background. Has Nested Sampling in it
        too, but those sections are more challenging.<br>
    </p>

    <p class="small">
        <b>Bayesian Logical Data Analysis for the Physical Sciences</b>
        by Phil Gregory. A bit more comprehensive than Sivia, and includes more
        detail on the Metropolis algorithm. Again, good for people with some
        physics background.
    </p>

    <p class="small">
        <b>Kendall's Advanced Theory of Statistics: Volume 2B: Bayesian Inference</b>
        by O'Hagan and Forster. A nice and fairly comprehensive textbook. The
        style and notation, and the examples given, are more like what
        statisticians use, so this is good for people from a statistics
        background.
    </p>

    <p class="small">
        <b>STATS 331 Lecture notes</b> by me!
        A very gentle introduction, with some R and JAGS. Aimed mostly at
        statistics students but should work for anyone.
        <a href="https://www.stat.auckland.ac.nz/~brewer/stats331.pdf"
        target="new">Available here</a>.
    </p>

    <p class="small">
        <b>Doing Bayesian Data Analysis</b> by John Kruschke.
        Probably the best full textbook for beginners with a statistics
        background.
    </p>

    <p class="small">
        <b>Information Theory, Inference, and Learning Algorithms</b>
        by David MacKay (RIP). A very readable and engaging textbook.
        Covers a wide range of topics apart from Bayes.
        And there's a free, legal online PDF file of it!
        <a href="http://www.inference.phy.cam.ac.uk/itprnn/book.html"
        target="new">Available here</a>.
    </p>

    <a name="nestedsampling"></a><h2>Nested Sampling</h2>

    <p class="small">
        Nested Sampling is my favourite Bayesian computation algorithm. I like
        to use it in combination with the Metropolis algorithm.
        My book chapter
        <a href="https://www.stat.auckland.ac.nz/~brewer/wsbook.pdf"
           target="new">Bayesian inference and computation: A beginner's
                        guide</a>
        has a fairly gentle introduction. The corresponding Python code
        is available <a href="https://github.com/eggplantbren/NSwMCMC"
                        target="new">here</a>.
    </p>

    <p class="small">
        The original Nested Sampling paper, by John Skilling, is very
        useful, but isn't easy to read. There are a few different versions
        floating around, but I usually use the one published in the
        Bayesian Analysis journal.
        <a href="http://projecteuclid.org/download/pdf_1/euclid.ba/1340370944"
           target="new">Here it is</a>.
    </p>

    <p class="small">
        I have an implementation of Nested Sampling
        <a href="https://github.com/eggplantbren/NestedSampling.jl"
            target="new">written in Julia</a>.
        With <a href="http://jtobin.io/" target="new">Jared Tobin</a>, I also
        wrote an implementation
        <a href="https://github.com/eggplantbren/NestedSampling.hs"
           target="new">in Haskell</a>.
    </p>

    <a name="dns"></a><h2>Diffusive Nested Sampling</h2>
    <p class="small">
        Diffusive Nested Sampling is an alternative to standard NS, that still
        uses the Metropolis algorithm to move around. Here is the
        <a href="https://arxiv.org/abs/1606.03757"
           target="new">original paper describing the algorithm</a>,
        the <a href="https://github.com/eggplantbren/DNest4"
               target="new">software</a>, and the
        <a href="https://github.com/eggplantbren/DNest4/raw/master/paper/static_pdf.pdf" target="new">paper describing the software</a>.
    </p>

    <a name="computing"></a><h2>Computing</h2>
    <p class="small">
        It's worth getting good at using the command line of a Unix-like
        operating system such as Mac OS X or Linux (e.g., Ubuntu).
        If you only use Windows, that's okay, but it might be a bit more
        annoying figuring out how to use certain things.
    </p>

    <p class="small">
        I recommend learning and using a version control system such as
        git. There probably isn't much point for a 30-point project student,
        but beyond that, it's pretty indispensible.
        <a href="http://rogerdudler.github.io/git-guide/" target="new">This
            tutorial</a> seems like a promising starting point.
    </p>

   <p class="small">
        My favourite book on C++ is
        <a href="https://www.goodreads.com/book/show/11277418-professional-c"
           target="new">Professional C++ by Marc Gregoire</a>.
        It assumes you have a reasonable amount of programming experience.
    </p>

   <p class="small">
        My favourite book on Haskell is
        <a href="https://www.goodreads.com/book/show/25587599-haskell-programming"
           target="new">Haskell Programming from First Principles</a>
        by Chris Allen and Julie Moronuki.
    </p>

   <p class="small">
        I haven't read any books on Python, but I followed Allen Downey
        on Twitter for a while so maybe <a href="https://www.goodreads.com/book/show/14514306-think-python?from_search=true" target="new">try his one</a>.
        Use Python 3, and make sure anything you find is teaching it. Forget
        about Python 2. A good Python distribution with useful packages for
        scientific stuff is
        <a href="https://www.continuum.io/downloads" target="new">Anaconda</a>.
    </p>

   <p class="small">
        Many of you will have learned one or two programming languages
        before, but will have to learn another one in order to work well
        with me. When I have had to do this in the past, especially
        for numerical work, I've found various "cheat sheets" useful,
        which show how to do the same thing in several different
        languages, side by side. Here are a couple of links which
        might help:<br><br>

        <a href="http://mathesaurus.sourceforge.net/r-numpy.html"
           target="new">NumPy for R programmers</a><br>
        <a href="http://hyperpolyglot.org/" target="new">Hyperpolyglot</a>
   </p>

    </div> <!-- The div around everything -->

</body>

</html>
	<!DOCTYPE html>
	<html>

	<head>
	<!-- CSS classes -->
	<style type="text/css">
	.small { font-size: 0.9em }

	/* To keep the page narrowish */
	div.content { width: 600px }
	</style>
	<title>Resources for research students</title>
	</head>

	<body>
	<div class="content">

	<!-- Welcome paragraph -->
	<h1>Welcome</h1>
	<p>
	Here, I'll collect links to resources for learning various tools
	and methods, some of which will be useful for research students working
	with me. I'll generally only recommend things I've used or read myself.
	Some of these are online resources, others are books that may or may
	not be available at the Auckland uni library or at Auckland libraries
	in general.
	You are encouraged to search for other ones as well!
	</p>

	<p>
	<a href="#bayes">Bayesian Inference</a><br>
	<a href="#nestedsampling">Nested Sampling</a><br>
	<a href="#dns">Diffusive Nested Sampling</a><br>
	<a href="#computing">Computing</a>
	</p>

	<!-- Resources for Bayesian inference -->
	<a name="bayes"></a><h2>Bayesian inference</h2>

	<p class="small">
	<b>Data Analysis: A Bayesian Tutorial</b> by Sivia and Skilling.
	Good for beginners with a physics background. Has Nested Sampling in it
	too, but those sections are more challenging.<br>
	</p>

	<p class="small">
	<b>Bayesian Logical Data Analysis for the Physical Sciences</b>
	by Phil Gregory. A bit more comprehensive than Sivia, and includes more
	detail on the Metropolis algorithm. Again, good for people with some
	physics background.
	</p>

	<p class="small">
	<b>Kendall's Advanced Theory of Statistics: Volume 2B: Bayesian Inference</b>
	by O'Hagan and Forster. A nice and fairly comprehensive textbook. The
	style and notation, and the examples given, are more like what
	statisticians use, so this is good for people from a statistics
	background.
	</p>

	<p class="small">
	<b>STATS 331 Lecture notes</b> by me!
	A very gentle introduction, with some R and JAGS. Aimed mostly at
	statistics students but should work for anyone.
	<a href="https://www.stat.auckland.ac.nz/~brewer/stats331.pdf"
	target="new">Available here</a>.
	</p>

	<p class="small">
	<b>Doing Bayesian Data Analysis</b> by John Kruschke.
	Probably the best full textbook for beginners with a statistics
	background.
	</p>

	<p class="small">
	<b>Information Theory, Inference, and Learning Algorithms</b>
	by David MacKay (RIP). A very readable and engaging textbook.
	Covers a wide range of topics apart from Bayes.
	And there's a free, legal online PDF file of it!
	<a href="http://www.inference.phy.cam.ac.uk/itprnn/book.html"
	target="new">Available here</a>.
	</p>

	<a name="nestedsampling"></a><h2>Nested Sampling</h2>

	<p class="small">
	Nested Sampling is my favourite Bayesian computation algorithm. I like
	to use it in combination with the Metropolis algorithm.
	My book chapter
	<a href="https://www.stat.auckland.ac.nz/~brewer/wsbook.pdf"
	target="new">Bayesian inference and computation: A beginner's
	guide</a>
	has a fairly gentle introduction. The corresponding Python code
	is available <a href="https://github.com/eggplantbren/NSwMCMC"
	target="new">here</a>.
	</p>

	<p class="small">
	The original Nested Sampling paper, by John Skilling, is very
	useful, but isn't easy to read. There are a few different versions
	floating around, but I usually use the one published in the
	Bayesian Analysis journal.
	<a href="http://projecteuclid.org/download/pdf_1/euclid.ba/1340370944"
	target="new">Here it is</a>.
	</p>

	<p class="small">
	I have an implementation of Nested Sampling
	<a href="https://github.com/eggplantbren/NestedSampling.jl"
	target="new">written in Julia</a>.
	With <a href="http://jtobin.io/" target="new">Jared Tobin</a>, I also
	wrote an implementation
	<a href="https://github.com/eggplantbren/NestedSampling.hs"
	target="new">in Haskell</a>.
	</p>

	<a name="dns"></a><h2>Diffusive Nested Sampling</h2>
	<p class="small">
	Diffusive Nested Sampling is an alternative to standard NS, that still
	uses the Metropolis algorithm to move around. Here is the
	<a href="https://arxiv.org/abs/1606.03757"
	target="new">original paper describing the algorithm</a>,
	the <a href="https://github.com/eggplantbren/DNest4"
	target="new">software</a>, and the
	<a href="https://github.com/eggplantbren/DNest4/raw/master/paper/static_pdf.pdf" target="new">paper describing the software</a>.
	</p>

	<a name="computing"></a><h2>Computing</h2>
	<p class="small">
	It's worth getting good at using the command line of a Unix-like
	operating system such as Mac OS X or Linux (e.g., Ubuntu).
	If you only use Windows, that's okay, but it might be a bit more
	annoying figuring out how to use certain things.
	</p>

	<p class="small">
	I recommend learning and using a version control system such as
	git. There probably isn't much point for a 30-point project student,
	but beyond that, it's pretty indispensible.
	<a href="http://rogerdudler.github.io/git-guide/" target="new">This
	tutorial</a> seems like a promising starting point.
	</p>

	<p class="small">
	My favourite book on C++ is
	<a href="https://www.goodreads.com/book/show/11277418-professional-c"
	target="new">Professional C++ by Marc Gregoire</a>.
	It assumes you have a reasonable amount of programming experience.
	</p>

	<p class="small">
	My favourite book on Haskell is
	<a href="https://www.goodreads.com/book/show/25587599-haskell-programming"
	target="new">Haskell Programming from First Principles</a>
	by Chris Allen and Julie Moronuki.
	</p>

	<p class="small">
	I haven't read any books on Python, but I followed Allen Downey
	on Twitter for a while so maybe <a href="https://www.goodreads.com/book/show/14514306-think-python?from_search=true" target="new">try his one</a>.
	Use Python 3, and make sure anything you find is teaching it. Forget
	about Python 2. A good Python distribution with useful packages for
	scientific stuff is
	<a href="https://www.continuum.io/downloads" target="new">Anaconda</a>.
	</p>

	<p class="small">
	Many of you will have learned one or two programming languages
	before, but will have to learn another one in order to work well
	with me. When I have had to do this in the past, especially
	for numerical work, I've found various "cheat sheets" useful,
	which show how to do the same thing in several different
	languages, side by side. Here are a couple of links which
	might help:<br><br>

	<a href="http://mathesaurus.sourceforge.net/r-numpy.html"
	target="new">NumPy for R programmers</a><br>
	<a href="http://hyperpolyglot.org/" target="new">Hyperpolyglot</a>
	</p>

	</div> <!-- The div around everything -->

	</body>

	</html>