Name: Edward Ionides
Type: User
Company: University of Michigan
Bio: I am Professor of Statistics at University of Michigan. I study time series analysis with applications including ecology and epidemiology.
Location: Ann Arbor
401f18
Class materials for STATS 401 (Fall 2018) Applied Statistical Methods II
401w18
Statistics 401: Applied Statistical Methods II
531w16
Course materials for Stats 531 Winter 2016 (Analysis of Time Series)
531w18
Analysis of Time Series
531w20
STATS 531 (Winter 2020) ‘Analysis of Time Series’
531w21
Stats 531 (Winter 2021) ‘Analysis of Time Series’ (https://ionides.github.io/531w21/)
531w22
Time series analysis (STATS/DATASCI 531, Winter 2022)
531w24
DATASCI/STATS 531. Modeling and analysis of time series data.
810f15
Class materials for Stats 810 Fall 2015, a proseminar on responsible scientific conduct and research methods
810f19
Course site for STATS 810 "Literature proseminar"
810f20
Course site for STATS 810 (Fall 2020) Literature proseminar, including discussions on responsible conduct in research and scholarship
810f21
Course materials for STATS 810 Fall 2021, Literature proseminar with emphasis on responsible conduct of research and scholarship
810f22
Course materials for STATS 810 Fall 2022
810f23
Course website STATS 810 Fall 2023 (Literature Proseminar 1)
bagged_filters
Code for Ionides, E.L., Asfaw, K., Park, J. and King, A.A., 2021. Bagged filters for partially observed interacting systems. Journal of the American Statistical Association. Pre-print at https://arxiv.org/abs/2002.05211.
ibpf-tutorial
ibpf_article
Source code for "An iterated block particle filter for inference on coupled dynamic systems with shared and unit-specific parameters"
if2
ionides.github.io
Github homepage for Edward Ionides
panelpomp
R package for statistical inference using panel POMPs (Partially Observed Markov Processes)
spatpomp
R package for statistical inference of spatiotemporal partially observed Markov processes
spatpomp-article
Source code for an article describing the spatPomp package
sweden21
Slides for Chalmers/Gothenburg seminar and other similar talks
usa_cyclical_mortality
Investigating the association between unemployment and mortality in USA national-level data
React
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Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
Django
The Web framework for perfectionists with deadlines.
Laravel
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
Microsoft
Open source projects and samples from Microsoft.
Google
Google ❤️ Open Source for everyone.
Alibaba
Alibaba Open Source for everyone
D3
Data-Driven Documents codes.
Tencent
China tencent open source team.
