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Articles by Eric Novik
Bayesian Machine Learning Model Guiding Iterative, Personalized Anticoagulant Dosing Decision-Making : ENGAGE AF-TIMI 48 Trial Analysis:
ABSTRACT Anticoagulation in atrial fibrillation often relies on a fixed dose, infrequent dose adjustment and does not incorporate patient preferences for risks of stroke, bleeding, and death. We developed a Bayesian ML model (Adele) to guide individualized long-term dosing accounting for patient preferences. Adele is a Bayesian competing-risk, multistate hazard model trained on 5,380 edoxaban-treated AF patients with pharmacokinetic (PK) data from the ENGAGE AF-TIMI 48 trial.
The Scientific Method Vs. AI: Is There Still Room For Theory?
Eric Novik is a cofounder and CEO of Generable and an Adjunct Professor of Statistics at NYU. A lot of ink has been spilled on the question of what will ultimately win: the scientific method, an approach to learning about the world by coming up with theories and testing those theories against observations; or the theory-free AI method, throwing enormous datasets at an artificial neural network or another universal function approximator and testing the predictions against observations.
Statistical Modeling, Causal Inference, and Social Science
Το Neon54 Casino δέχεται κρυπτονομίσματα και νομίσματα fiat που περιλαμβάνουν ευρώ, καναδικά δολάρια, δολάρια ΗΠΑ, ιαπωνικά γεν, ουγγρικά φιορίνια, νορβηγική κορώνα, ρωσικά ρούβλια και άλλα. This is Eric. Brian Parbhu took over the Bayesian Data Analysis meetup (formerly NYC Stan Users Group), and he is running an event in NYC this Friday, during which Brian Ward is going to discuss BridgeStan, the new in-memory interface for Stan. You can register here.
A two-week course focused on basic math, probability, and statistics skills
This post is by Eric. On August 15, I will be teaching this two-week course offered by the PRIISM center at NYU. The initial plan was to offer it to the NYU students entering the A3SR MS Program, but we are opening it up to a wider audience. In case you don’t like clicking on things, here is a short blurb: This is a paid class, but Jenniffer Hill, who runs the program, tells me that department scholarships are available based on program and student needs.
Webinar: On using expert information in Bayesian statistics
Post navigation Don’t believe the “Breaking News” hype (NYT science section version) This post is by Eric. On Thursday, 23 June, Duco Veen will stop by to discuss his work on prior elicitation. You can register here. Abstract Duco will discuss how expert knowledge can be captured and formulated as prior information for Bayesian analyses. This process is also called expert elicitation.
Statistical Modeling, Causal Inference, and Social Science
Post navigation Yelp reviews for scientists and pundits? This post is by Eric. On Wednesday, Juho Timonen from Aalto University is stopping by to tell us about his work. You can register here. Abstract Juho will present what he thinks is an ideal modular design for statistical modeling software, based on his experiences as a user, developer, and researcher. He will look at the structure of existing software, focusing on high-level user interfaces that use Stan as their inference engine.
Webinar: Making synthetic data look real using conditional GANs
This post is by Eric. On Thursday, Maria Skoularidou from the University of Cambridge, Biostatistics Unit is stopping by to talk to us about generating high-quality synthetic data. You can register here. You can read the whole paper here. Abstract Modeling the probability distribution of rows in tabular data and generating realistic synthetic data is a non-trivial task.
Webinar: Predicting future forest tree communities and winegrowing regions with Stan
This post is by Eric. On Friday, Elizabeth Wolkovich from the University of British Columbia is stopping by to talk to us about her work. You can register here. Abstract Climate change is having large impacts on natural and agricultural systems around the globe. Mitigating the worst consequences requires models that mechanistically predict changes.
Statistical Modeling, Causal Inference, and Social Science
This post is by Eric. Next Thursday, Rok Češnovar is stopping by to talk to us about recent developments in and around Stan. You can register here. Abstract We will present the current state of the Stan ecosystem, highlight some of the advances that improved the performance of Stan in the past few years, and discuss what is still to come in the not-so-distant future.
Statistical Modeling, Causal Inference, and Social Science
This post is by Eric. Tomorrow, we will be hosting Lester Mackey from Microsoft Research. You can register here.
Webinar: Towards responsible patient-level causal inference: taking uncertainty seriously
Webinar: Towards responsible patient-level causal inference: taking uncertainty seriously Posted by Eric Novik on 28 September 2021, 3:05 pm This post is by Eric. We are resuming our Webinar series this Thursday with Uri Shalit from Technion. You can register here. Abstract A plethora of new methods for estimating patient-level causal effects have been proposed recently, focusing on what is technically known as (high-dimensional) conditional average effects (CATE).
Webinar: A Gaussian Process Model for Response Time in Conjoint Surveys
Webinar: A Gaussian Process Model for Response Time in Conjoint Surveys Posted by Eric Novik on 19 July 2021, 3:54 pm This post is by Eric. This Wednesday, at 11:30 am ET, Elea Feit is stopping by to talk to us about her recent work on Conjoint models fit using GPs. You can register here. Abstract Choice-based conjoint analysis is a widely-used technique for assessing consumer preferences.
Statistical Modeling, Causal Inference, and Social Science
Webinar: Theories of Inference for Data Interactions Posted by Eric Novik on 14 June 2021, 3:56 pm This post is by Eric. This Thursday, at 12 pm ET, Jessica Hullman is stopping by to talk to us about theories of inference for data interactions. You can register here.
Webinar: Fast Discovery of Pairwise Interactions in High Dimensions using Bayes
Webinar: Fast Discovery of Pairwise Interactions in High Dimensions using Bayes Posted by Eric Novik on 17 May 2021, 4:42 pm This post is by Eric. This Wednesday, at 12 pm ET, Tamara Broderick is stopping by to talk to us about pairwise interactions in high dimensions. You can register here. Abstract Discovering interaction effects on a response of interest is a fundamental problem in medicine, economics, and many other disciplines.
Webinar: An introduction to Bayesian multilevel modeling with brms
Webinar: An introduction to Bayesian multilevel modeling with brms Posted by Eric Novik on 13 April 2021, 3:31 pm This post is by Eric. This Wednesday, at 12 pm ET, Paul Bürkner is stopping by to talk to us about brms. You can register here. Abstract The talk will be about Bayesian multilevel models and their implementation in R using the package brms.
Statistical Modeling, Causal Inference, and Social Science
Webinar: On Bayesian workflow Posted by Eric Novik on 15 March 2021, 3:10 pm This post is by Eric. This Wednesday, at 12 pm ET, Aki Vehtari is stopping by to talk to us about Bayesian workflow. You can register here. Abstract We will discuss some parts of the Bayesian workflow with a focus on the need and justification for an iterative process.
Webinar: Some Outstanding Challenges when Solving ODEs in a Bayesian context
Webinar: Some Outstanding Challenges when Solving ODEs in a Bayesian context Posted by Eric Novik on 15 February 2021, 3:51 pm This post is by Eric. This Wednesday, at 12 pm ET, Charles Margossian is stopping by to talk to us about solving ODEs using Bayesian methods. You can register here.
Webinar: Functional uniform priors for dose-response models
Webinar: Functional uniform priors for dose-response models Posted by Eric Novik on 18 January 2021, 3:17 pm This post is by Eric. This Wednesday, at 12 pm ET, Kristian Brok is stopping by to talk to us about functional uniform priors for dose-response models. You can register here. Abstract Dose-response modeling frequently employs non-linear regression.
Statistical Modeling, Causal Inference, and Social Science
Nonparametric Bayes webinar Posted by Eric Novik on 15 November 2020, 3:05 pm This post is by Eric. A few months ago we started running monthly webinars focusing on Bayes and uncertainty. Next week, we will be hosting Arman Oganisian, a 5th-year biostatistics PhD candidate at the University of Pennsylvania and Associate Fellow at the Leonard Davis Institute for Health Economics.
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