Publications by R on Will Hipson
Modeling Motivation and Emotion using Feedback Loops
If you’re anything like me, you probably set a lot of goals. Whether it’s to finish a paper by the end of the summer or to spend more time with friends and family, goals are what help motivate us to do something. Goals are also intimately tied to our feelings. You may have had the experience of falling behind in your goals, which made you ups...
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Bayesian Linear Mixed Models: Random Intercepts, Slopes, and Missing Data
This past summer, I watched a brilliant lecture series by Richard McElreath on Bayesian statistics. It honestly changed my whole outlook on statistics, so I couldn’t recommend it more (plus, McElreath is an engaging instructor). One of the most compelling cases for using Bayesian statistics is with a collection of statistical tools called linea...
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Building a Shiny App for Cycling in Ottawa
This is a different kind of post, but one that I think is kind of fun. I currently live in Ottawa, which for those who don’t know, is the capital city of Canada. For a capital city, it’s fairly small, but it’s increasingly urbanizing (we just got lightrail transit). Segregated bicycle lanes and paths are becoming more common too...
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Building a Shiny App for Cycling in Ottawa
This is a different kind of post, but one that I think is kind of fun. I currently live in Ottawa, which for those who don’t know, is the capital city of Canada. For a capital city, it’s fairly small, but it’s increasingly urbanizing (we just got lightrail transit). Segregated bicycle lanes and paths are becoming more common too and many of...
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An Intuitive Look at Binomial Probability in a Bayesian Context
Binomial probability is the relatively simple case of estimating the proportion of successes in a series of yes/no trials. The perennial example is estimating the proportion of heads in a series of coin flips where each trial is independent and has possibility of heads or tails. Because of its relative simplicity, the binomial case is...
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An Intuitive Look at Binomial Probability in a Bayesian Context
Binomial probability is the relatively simple case of estimating the proportion of successes in a series of yes/no trials. The perennial example is estimating the proportion of heads in a series of coin flips where each trial is independent and has possibility of heads or tails. Because of its relative simplicity, the binomial case is a great pla...
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Visualizing a Markov Chain
A Markov Chain describes a sequence of states where the probability of transitioning from states depends only the current state. Markov chains are useful in a variety of computer science, mathematics, and probability contexts, also featuring prominently in Bayesian computation as Markov Chain Monte Carlo. Here, we’re going to look a...
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Visualizing a Markov Chain
A Markov Chain describes a sequence of states where the probability of transitioning from states depends only the current state. Markov chains are useful in a variety of computer science, mathematics, and probability contexts, also featuring prominently in Bayesian computation as Markov Chain Monte Carlo. Here, we’re going to look at a relative...
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Bayesian Varying Effects Models in R and Stan
In psychology, we increasingly encounter data that is nested. It is to the point now where any quantitative psychologist worth their salt must know how to analyze multilevel data. A common approach to multilevel modeling is the varying effects approach, where the relation between a predictor and an outcome variable is modeled both wit...
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Bayesian Varying Effects Models in R and Stan
In psychology, we increasingly encounter data that is nested. It is to the point now where any quantitative psychologist worth their salt must know how to analyze multilevel data. A common approach to multilevel modeling is the varying effects approach, where the relation between a predictor and an outcome variable is modeled both within clusters...
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