Published Pages

a selected portfolio

[December 2022]
**
Curriculum Vitae.
**
my long-form resume

tags: dustin lennon, applied scientist, statistician

[February 2021]
**
Personalized Point Processes: A Simple Bayesian Analysis.
**
This post describes a homogeneous Poisson process using a Gamma conjugate prior that can be used to estimate a pooled, per-subject intensity given a collection of realizations.

tags: point process, bayesian, multiple realizations

[February 2021]
**
Doubly and Randomly Censored Data.
**
A derivation of the density functions and likelihood expression associated with doubly and randomly censored data.

tags: censored data, likelihood, distribution, derivation, event data

[September 2018]
**
Probability Integral Transform, A Proof.
**
This post elucidates the connection between the generalized inverse, the cdf, the quantile function, and the uniform distribution.

tags: statistics, theory, probability integral transform, proof

[September 2016]
**
Adaptive Rejection Sampling.
**
This post describes and implements an adaptive rejection sampler for log-concave densities.

tags: adaptive sampling, rejection sampling, logconcave density, distribution

[November 2012]
**
Probabilistic Performance Guarantees for Oversubscribed Resources.
**
This paper constructs a model for shared resource utilization, determines stochastic bounds for resource exhaustion, and simulates results.

tags: simulation, stochastic resource allocation

[April 2021]
**
Metadata by Design.
**
We describe a Pandas extention of the categorical dtype that better encapsulates metadata.

tags: pandas, extension, categorical data, metadata

[October 2019]
**
An Introduction to pytrope.psycopg2_extras.
**
This notebook serves as an introduction to the pytrope.psycopg2_extras package.

tags: python, matplotlib, pytrope, helper methods

[October 2019]
**
An Introduction to pytrope.matplotlib_extras.
**
This notebook serves as an introduction to the pytrope.matplotlib_extras package.

tags: python, matplotlib, pytrope, helper methods

[December 2020]
**
Cash Flow Considerations for Machine Learning.
**
A recent job talk

tags: cash flow, loans, survival analysis, logrank statistic, machine learning, lending club

[September 2019]
**
[GH07] Figure 3.1: Mothers' education and children's test scores.
**
This notebook replicates results in GH07, section 3.1, using (a) the statsmodels package and (b) the pystan package.

tags: statsmodels, pystan, linear regression, bayesian analysis, non-informative priors

[October 2015]
**
Uber Interview Challenge.
**
My take on an Uber data-science assignment.

tags: uber, pre-interview, homework

[October 2013]
**
Optimal Lending Club Portfolios.
**
The paper characterizes an optimal, managed portfolio with annual returns in excess of 12%.

tags: lending club, optimal portfolios, survival random forest, machine learning

[August 2019]
**
Unlocking TrueType Fonts: Fontforge, Matplotlib, and Partially Ordered Occlusions.
**
This post shows how to merge ttf files and manipulate truetype glyphs.

tags: python, fontforge, matplotlib, shapely

[August 2019]
**
Published Pages Cheatsheet.
**
This page catalogs the features available on the Published Pages framework.

tags: publishing, framework, feature overview

[July 2019]
**
Recommendation Systems, A Mathematical Overview.
**
This post provides an overview of recommendation systems and algorithms.

tags: machine learning, recommendation systems, algorithms

[July 2019]
**
Recommending a Data Warehouse.
**
This webpage gives an overview of Gartner's 2019 'critical capabilities' analysis and showcases a webtool that provides personalized recommendations.

tags: data warehouse, planning, personalization, gartner

[August 2015]
**
Namecheap, Dynamic IP Addresses, and Hosting Multiple Sites.
**
This post shows how to augment Namecheap's ddclient script to support multiple hosts on a dynamic IP.

tags: namecheap, ddclient, dynamic ip address, multiple sites

[March 2021]
**
Annotations: Elements of Statistical Learning.
**
My margin notes from reading Hastie, Tibshirani, and Friedman

tags: notes, elements of statistical learning, machine learning