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and set `future: false`

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The plots below show examples of ideological topics for U.S. Senate speeches (2015-2017) based on the methodology of our paper, **Text-Based Ideal Points**. Move the slider to see how the ideological topic changes as a function of ideal point:

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A couple of weeks ago, I wrote about variational inference for probit regression, which involved some pretty ugly algebra. Although variational inference is a powerful method for approximate Bayesian inference, it can be tedious to come up with the variational updates for every model (which aren’t always available in closed-form), and these updates are model-specific.

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A couple of weeks ago, I wrote a blog post about modeling ideal points of US senators. I wanted to follow up (very briefly), since I was curious about comparing the Bayesian method there with Principal Component Analysis (PCA).

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Variational inference has become one of the most important approximate inference techniques for Bayesian statistics, but it has taken me a long time to wrap my head around the central ideas (and I’m still learning). Since I’ve found that going through examples is the most efficient way to learn, I thought I would go through a single example in this post, performing variational inference on Bayesian probit regression.

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Popularized by Keith Poole and Howard Rosenthal, ideal point modeling is a powerful way to extract the relative ideologies of politicans based solely on their voting records. A lot has been written on ideal point models, so I’m not going to add anything new, but I wanted to give a brief overview of the Bayesian perspective.

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Every statistician has a favorite way of generating samples from a distribution (not sure if I need a citation for this one). From rejection sampling to Hamiltonian Monte Carlo, there are countless methods to choose from (my personal favorite is `rnorm`

).

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To paraphrase Benjamin Disraeli, statistics makes it easy to lie. In this post, I’ll go over an example from Judea Pearl’s excellent textbook, Causality, that shows how different statistical approaches can lead to different estimates of the causal effect of smoking on lung cancer.

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*Last week, I wrote about modeling tweet counts as a simple Poisson process. In this post, I’ll dive into a slightly more sophisticated method, so check out the previous post for some background.*

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I’ve written before on using statistics to bet on PredictIt’s political markets, and this morning a new market caught my eye: How many tweets will Donald Trump post from noon Feb. 1 to noon Feb. 8?. If statistics can be used to inform any prediction market, it’s this one, so I figured I’d give this counting problem a go.

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Trump’s inauguration was yesterday, and we’re all coping with it in different ways. Instead of watching yesterday’s events, I decided to download a bunch of inaugural addresses and make some word clouds (jump ahead a little bit if you’re curious about the more technical details).

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Recently I’ve become interested in causal inference, as I’ve been exploring both the foundational approaches and the more recent applications to machine learning. An important application is genome-wide association studies (GWAS), where biologists attempt to uncover the causal link between genotypes and traits of interest (i.e. what part of the genome causes orange hair?).

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In this post, I’m going to delve into a result from the paper Surprised by the Gambler’s and Hot Hand Fallacies? A Truth in the Law of Small Numbers by Joshua Miller and Adam Sanjurjo. The paper covers a really counterintuitive result, so I recommend checking it out.

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*This is the second part of a two-part blog post on Gaussian processes. If you would like an overview of Gaussian processes, head over to the first part.*

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*This is the first part of a two-part blog post on Gaussian processes. If you would like to skip this overview and go straight to making money with Gaussian processes, jump ahead to the second part.*

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

** Published:**

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

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Short description of portfolio item number 1

Short description of portfolio item number 2

Published in *Journal 1*, 2009

This paper is about the number 1. The number 2 is left for future work.

Recommended citation: Your Name, You. (2009). "Paper Title Number 1." *Journal 1*. 1(1). __http://academicpages.github.io/files/paper1.pdf__

Published in *Journal 1*, 2010

This paper is about the number 2. The number 3 is left for future work.

Recommended citation: Your Name, You. (2010). "Paper Title Number 2." *Journal 1*. 1(2). __http://academicpages.github.io/files/paper2.pdf__

Published in *Journal 1*, 2015

This paper is about the number 3. The number 4 is left for future work.

Recommended citation: Your Name, You. (2015). "Paper Title Number 3." *Journal 1*. 1(3). __http://academicpages.github.io/files/paper3.pdf__

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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown!

** Published:**

This is a description of your conference proceedings talk, note the different field in type. You can put anything in this field.

Undergraduate course, *University 1, Department*, 2014

This is a description of a teaching experience. You can use markdown like any other post.

Workshop, *University 1, Department*, 2015

This is a description of a teaching experience. You can use markdown like any other post.