Fixed titles, math rendering, and links on some pages

content/notes/abstract2def.md

@@ -1,4 +1,10 @@
-# Abstract Algebra 2 Definitions 
+---
+title: Abstract Algebra Notes
+showthedate: false
+math: true
+---
+
+Chapter markings are based off the book "A Book of Abstract Algebra" by Charles C. Pinter.
 
 ## Chapter 17
 

content/notes/algorithms.md

@@ -2,7 +2,6 @@
 title: Algorithms Book Study
 ---
 
-# Algorithms Book Study
 A couple of my friends and I decided to start a book club following "Algorithms" by Jeff Erickson. One bonus is that he gives it away for free on [his website](http://jeffe.cs.illinois.edu/teaching/algorithms/)!
 
 Of course you should totally check his book out rather than reading my notes. There are tons of witty and fun things in his textbook, not a dry reading I promise. These notes are here mostly for archival purposes.
@@ -14,4 +13,4 @@ Of course you should totally check his book out rather than reading my notes. Th
 
 [Chapter 3](dynamic)
 
-[Chapter 4](greedy)
+[Chapter 4](greedy)

content/notes/algorithms/backtracking.md

@@ -1,4 +1,8 @@
-# Backtracking
+---
+title: Backtracking
+showthedate: false
+---
+
 This algorithm tries to construct a solution to a problem one piece at a time. Whenever the algorithm needs to decide between multiple alternatives to the part of the solution it *recursively* evaluates every option and chooses the best one.
 
 
@@ -59,4 +63,4 @@ ConstructSubset(X, i, T):
 
 Backtracking algorithms are used to make a *sequence of decisions*.
 
-When we design a new recursive backtracking algorithm, we must figure out in advance what information we will need about past decisions in the middle of the algorithm.
+When we design a new recursive backtracking algorithm, we must figure out in advance what information we will need about past decisions in the middle of the algorithm.

content/notes/algorithms/dynamic.md

@@ -1,4 +1,6 @@
-# Dynamic Programming
+---
+title: Dynamic Programming
+---
 
 The book first goes into talking about the complexity of the Fibonacci algorithm
 
@@ -79,4 +81,4 @@ These algorithms are best developed in two distinct stages.
 
 ## Greedy Algorithms
 
-If we're lucky we can just make decisions directly instead of solving any recursive subproblems. The problem is that greedly algorithms almost never work.
+If we're lucky we can just make decisions directly instead of solving any recursive subproblems. The problem is that greedly algorithms almost never work.

content/notes/algorithms/greedy.md

@@ -1,4 +1,7 @@
-# Greedy Algorithms
+---
+title: Greedy Algorithms
+showthedate: false
+---
 
 Greedy Algorithms are about making the best local choice and then blindly plowing ahead.
 
@@ -33,4 +36,4 @@ An unstable match is when
 The Gale-Shapley algorithm is a great greedy fit. It goes like this
 
 1. An arbitrary unmatched hospital A offers its position to the best doctor a who has not already rejected it.
-2. If a is unmatched, she tentatively accepts A's offer. If a already had a match but prefers A, she rejects her current match and tentatively accepts the new offer from A. Otherwise a rejects the new offer.
+2. If a is unmatched, she tentatively accepts A's offer. If a already had a match but prefers A, she rejects her current match and tentatively accepts the new offer from A. Otherwise a rejects the new offer.

content/notes/algorithms/recursion.md

@@ -1,4 +1,8 @@
-# Recursion
+---
+title: Recursion
+showthedate: false
+math: true
+---
 
 ## Reductions
 

content/notes/bayesianstatistics.md

@@ -1,12 +1,8 @@
 ---
-title: Bayesian Statistics
+title: Bayesian Statistics - From Concept to Data Analysis
 showthedate: false
 ---
 
-
-
-# Bayesian Statistics: From Concept to Data Analysis
-
 In the Winter of 2017, I took a course on Bayesian Statistics on Coursera offered by Dr. Herbert Lee.
 
 Below are the notes for each of the four weeks.

content/notes/bayesianstatistics/week1.md

@@ -1,4 +1,8 @@
-# Bayesian Statistics
+---
+title: Week 1
+showthedate: false
+math: true
+---
 
 ## Rules of Probability
 

content/notes/bayesianstatistics/week2.md

@@ -1,3 +1,9 @@
+---
+title: Week 2
+showthedate: false
+math: true
+---
+
 Under the frequentest paradigm, you view the data as a random sample from some larger, potentially hypothetical population. We can then make probability statements i.e, long-run frequency statements based on this larger population.
 
 ## Coin Flip Example (Central Limit Theorem)
@@ -534,4 +540,4 @@ It may be fixed, but because we don't know what that value is, we represent our
 
 Frequentest confidence intervals have the interpretation that "If you were to repeat many times the process of collecting data and computing a 95% confidence interval, then on average about 95% of those intervals would contain the true parameter value; however, once you observe data and compute an interval the true value is either in the interval or it is not, but you can't tell which." 
 
-Bayesian credible intervals have the interpretation that "Your posterior probability that the parameter is in a 95% credible interval is 95%." 
+Bayesian credible intervals have the interpretation that "Your posterior probability that the parameter is in a 95% credible interval is 95%." 

content/notes/bayesianstatistics/week3.md

@@ -1,3 +1,9 @@
+---
+title: Week 3
+showthedate: false
+math: true
+---
+
 How do we choose a prior?
 
 Our prior needs to represent our personal perspective, beliefs, and our uncertainties.
@@ -406,4 +412,4 @@ After this, we need some other piece of knowledge to pin point both parameters.
 
 2. In Bayesian Statistics, a vague prior refers to one that's relatively flat across much of the space. For a Gamma prior we can choose $\Gamma(\epsilon, \epsilon)$ where $\epsilon$ is small and strictly positive.
 
-This would create a distribution with a mean of 1 and a huge standard deviation across the whole space. Hence the posterior will be largely driven by the data and very little by the prior.
+This would create a distribution with a mean of 1 and a huge standard deviation across the whole space. Hence the posterior will be largely driven by the data and very little by the prior.

content/notes/bayesianstatistics/week4.md

@@ -1,3 +1,9 @@
+---
+title: Week 4
+showthedate: false
+math: true
+---
+
 ## Exponential Data
 
 Suppose you're waiting for a bus that you think comes on average once every 10 minutes, but you're not sure exactly how often it comes.

content/notes/quadraticcongruences.md

@@ -1,4 +1,8 @@
-# Handy Quadratic Congruences Facts
+---
+title: Handy Facts about Quadratic Congruences
+showthedate: false
+math: true
+---
 
 ## Number of Solutions
 

content/notes/realanalysis.md

@@ -1,4 +1,8 @@
-# Real Analysis Sheet
+---
+title: Real Analysis Quick Sheet
+showthedate: false
+math: true
+---
 
 **Fact:** $\forall a,b, \in \mathbb{R}$, $\sqrt{ab} \le \frac{1}{2}(a + b)$.
 
@@ -90,4 +94,4 @@ Let $(x_n), (y_n)$ be strictly positive sequence of real numbers. Suppose $r = \
 
 (b) If ($r = 0$ and $\sum{y_n} < \infty$), then $\sum{x_n} < \infty$.
 
- 
+ 

content/notes/reproducibleresearch/week1.md

@@ -1,4 +1,7 @@
-# Reproducible Research Week 1
+---
+title: Week 1
+showthedate: false
+---
 
 ## Replication
 
@@ -394,4 +397,4 @@ It should tell a story
 
 It should not include every analysis you performed
 
-References should be included for statistical methods
+References should be included for statistical methods

content/notes/reproducibleresearch/week2.md

@@ -1,3 +1,8 @@
+---
+title: Week 2
+showthedate: false
+---
+
 ## Coding Standards for R
 
 1. Always use text files/text editor

content/notes/reproducibleresearch/week3.md

@@ -1,3 +1,8 @@
+---
+title: Week 3
+showthedate: false
+---
+
 ## tl;dr
 
 People are busy, especially managers and leaders. Results of data analyses are sometimes presented in oral form, but often the first cut is presented via email.
@@ -305,4 +310,4 @@ The premise of reproducible research is that with data/code available, people ca
 - Reproducible research focuses on the most "downstream" aspect of research documentation
 - Evidence-based data analysis would provide standardized best practices for given scientific areas and questions
 - Gives reviewers an important tool without dramatically increases the burden on them
-- More effort should be put into improving the quality of "upstream" aspects of scientific research
+- More effort should be put into improving the quality of "upstream" aspects of scientific research

content/notes/reproducibleresearch/week4.md

@@ -1,3 +1,8 @@
+---
+title: Week 4
+showthedate: false
+---
+
 ## The `cacher` Package for R
 
 - Add-on package for R

content/notes/stat381.md

@@ -3,10 +3,6 @@ title: Probability and Statistical Inference
 showthedate: false
 ---
 
-
-
-# Probability and Statistical Inference
-
 In the Fall of 2017, I took the course STAT 381 with Dr. Debra Hydorn. Below I included the interesting labs we worked on in the class.
 
 *Please note that these reports were not formatted for this site. So equations and images may not show up.*

content/notes/stat381/centrallimit.md

@@ -1,6 +1,8 @@
-# Central Limit Theorem Lab
-
-**Brandon Rozek**
+---
+title: Central Limit Theorem
+showthedate: false
+math: true
+---
 
 ## Introduction
 

content/notes/stat381/confint.md

@@ -1,6 +1,7 @@
-# Confidence Interval Lab
-
-**Written by Brandon Rozek**
+---
+title: Confidence Interval
+showthedate: false
+---
 
 ## Introduction
 

content/notes/stat381/randomnumber.md

@@ -1,4 +1,8 @@
-# Random Number Generation
+---
+title: Random Number Generation
+showthedate: false
+math: true
+---
 
 ## Introduction
 

content/notes/stat381/randomwalk.md

@@ -1,4 +1,8 @@
-# Random Walk
+---
+title: Random Walk
+showthedate: false
+math: true
+---
 
 ## Introduction