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Are you a researcher or data scientist / analyst / ninja? Do you want to learn Bayesian inference, stay up to date or simply want to understand what Bayesian inference is? Then this podcast is for you! You'll hear from researchers and practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the me ...
 
The Department of Statistics at Oxford is a world leader in research including computational statistics and statistical methodology, applied probability, bioinformatics and mathematical genetics. In the 2014 Research Excellence Framework (REF), Oxford's Mathematical Sciences submission was ranked overall best in the UK. This is an exciting time for the Department. We have now moved into our new home on St Giles and we are currently settling in. The new building provides improved lecture and ...
 
The health podcast series brings you highlights and data snapshots from the wide range of health data collected by the Australian Bureau of Statistics (ABS). The Health podcast will showcase this data in a series of short conversations that discuss Australia's health status following release of data from the suite of health surveys conducted by the ABS. The episodes will discuss a variety of topics, including health risk factors such as smoking and obesity, rates of physical activity and die ...
 
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Episode sponsored by Tidelift: tidelift.com I don’t know about you, but the notion of time is really intriguing to me: it’s a purely artificial notion; we humans invented it — as an experiment, I asked my cat what time it was one day; needless to say it wasn’t very conclusive… And yet, the notion of time is so central to our lives — our work, leisu…
 
Episode sponsored by Tidelift: tidelift.com I bet you already heard of Bayesian nonparametric models, at least on this very podcast. We already talked about Dirichlet Processes with Karin Knudson on episode 4, and then about Gaussian Processes with Elizaveta Semenova on episode 21. Now we’re gonna dive into the mathematical properties of these obje…
 
Karolina Dziugaite (Element AI), gives the OxCSML Seminar on 26th February 2021. Abstract: Deep learning approaches dominate in many application areas. Our understanding of generalization (relating empirical performance to future expected performance) is however lacking. In some applications, standard algorithms like stochastic gradient descent (SG…
 
Episode sponsored by Tidelift: tidelift.com One of the most common guest suggestions that you dear listeners make is… inviting Paul Bürkner on the show! Why? Because he’s a member of the Stan development team and he created BRMS, a popular R package to make and sample from Bayesian regression models using Stan. And, as I like you, I did invite Paul…
 
Professor Davina Durgana, award-winning international human rights statistician and professor with almost 15 years of experience developing leading global models to assess risk to modern slavery, gives a talk on their work on modern slavery. Abstract: Dr. Durgana will present her insights on the use of statistics in the global modern slavery vulner…
 
Bin Yu, Chancellor's Professor, Departments of Statistics and Electrical Engineering and Computer Science, UC Berkeley, gives a seminar for the Department of Statistics. 'A.I. is like nuclear energy - both promising and dangerous' - Bill Gates, 2019.Data Science is a pillar of A.I. and has driven most of recent cutting-edge discoveries in biomedica…
 
Episode sponsored by Tidelift: tidelift.com We already mentioned multilevel regression and post-stratification (MRP, or Mister P) on this podcast, but we didn’t dedicate a full episode to explaining how it works, why it’s useful to deal with non-representative data, and what its limits are. Well, let’s do that now, shall we? To that end, I had the …
 
How do people choose their career? How do they change jobs? How do they even change careers? These are important questions that we don’t have great answers to. But structured data about the dynamics of labor markets are starting to emerge, and that’s what Ben Zweig is modeling at Revelio Labs. An economist and data scientist, Ben is indeed the CEO …
 
Professor Kerrie Mengersen, Distinguished Professor of Statistics at Queensland University of Technology in the Science and Engineering Faculty, gives the The Corcoran Memorial Lecture, held on 21st January 2021. Abstract: The ability to generate, access and combine multiple sources of data presents both opportunity and challenge for statistical sc…
 
The Florence Nightingale Bicentennial Lecture was followed by a Panel Session with Professor Deborah Ashby, Professor David Cox and Professor David Spiegelhalter. The Panel was chaired by Professor Jennifer Rogers about the role of statistics in societyDeborah Ashby, David Cox, David Spiegelhalter による
 
When explaining Bayesian statistics to people who don’t know anything about stats, I often say that MCMC is about walking many different paths in lots of parallel universes, and then counting what happened in all these universes. And in a sense, this whole podcast is dedicated to sampling the whole distribution of Bayesian practitioners. So, for th…
 
Logistic regression is one of the most commonly used statistical analytic tools in the medical literature. William Meurer, MD, from the University of Michigan, and Juliana Tolles, MD, from UCLA, discuss a JAMA Guide to Statistics and Methods article they wrote entitled “Logistic Regression Diagnostics: Understanding How Well a Model Predicts Outcom…
 
Professor Deborah Ashby, President of the RSS, gives the 2020 Florence Nightingale lecture. Florence Nightingale, best known as the Lady with the Lamp, is recognised as a pioneering and passionate statistician. She was also passionate about education, having argued successfully with her parents to be allowed to study mathematics, and later nursing,…
 
Professor Deborah Ashby, President of the RSS, gives the 2020 Florence Nightingale lecture. Florence Nightingale, best known as the Lady with the Lamp, is recognised as a pioneering and passionate statistician. She was also passionate about education, having argued successfully with her parents to be allowed to study mathematics, and later nursing,…
 
I don’t know if you noticed, but I have a fondness for any topic related to decision-making under uncertainty — when it’s studied scientifically of course. Understanding how and why people make decisions when they don’t have all the facts is fascinating to me. That’s why I like electoral forecasting and I love cognitive sciences. So, for the first …
 
It’s funny how powerful symbols are, right? The Eiffel Tower makes you think of Paris, the Statue of Liberty is New-York, and the Trevi Fountain… is Rome of course! Just with one symbol, you can invoke multiple concepts and ideas. You probably know that symbols are omnipresent in mathematics — but did you know that they are also very important in s…
 
Part of the Probability for Machine Learning seminar series. Presented by Prof Lester Mackey (Microsoft Research New England and Stanford University). Abstract: Stein’s method is a powerful tool from probability theory for bounding the distance between probability distributions. In this talk, I’ll describe how this tool designed to prove central li…
 
Dr. Ekaterina Volkova-Volkmar, Senior Data Scientist, pRED Informatics - Data Science, Roche Pharma Research and Early Development, Roche, Basel, Switzerland, gives a talk on deep learning and graph neural networks in biomedicine.Ekaterina Volkova-Volkmar による
 
I’ll be honest here: I had a hard time summarizing this episode for you, and, let’s face it, it’s all my guest’s fault! Why? Because Aki Vehtari works on so many interesting projects that it’s hard to sum them all up, even more so because he was very generous with his time for this episode! But let’s try anyway, shall we? So, Aki is an Associate pr…
 
Jonny Brooks-Bartlett, Senior machine learning engineer at Spotify, gives a talk on his experiences as a data scientist and as machine learning engineer in top rated companies around the world. It's been almost 4 years since I left academia to work as a data scientist in industry. In that time I've worked in several teams at a couple of companies. …
 
In times of crisis, designing an efficient policy response is paramount. In case of natural disasters or pandemics, it can even determine the difference between life and death for a substantial number of people. But precisely, how do you design such policy responses, making sure that risks are optimally shared, people feel safe enough to reveal nec…
 
In a few days, a consequential election will take place, as citizens of the United States will go to the polls and elect their president — in fact they already started voting. You probably know a few forecasting models that try to predict what will happen on Election Day — who will get elected, by how much and with which coalition of States? But ho…
 
I don’t know about you, but I’m starting to really miss traveling and just talking to people without having to think about masks, social distance and activating the covid tracking app on my phone. In the coming days, there is one event that, granted, won’t make all of that disappear, but will remind me how enriching it is to meet new people — this …
 
Jason Forrest, Director of Interactive Data Visualization, COVID Response Centre, McKinsey and Co, New York, gives the Department of Statistics Black History Month lecture, with a talk on the work of African-American scholar and activist W.E.B. Du Bois. At the 1900 Paris Exposition, an all African-American team lead by scholar and activist W.E.B. D…
 
Have you watched the series « The English Game », on Netflix? Well, I think you should — it’s a fascinating dive into how football went from an aristocratic to a popular sport in the late 19th century England. Today it is so popular that it became a valuable business to do statistics on the game and its players! To talk about that, I invited Kevin …
 
Do you know what proteins are, what they do and why they are useful? Well, be prepared to be amazed! In this episode, Seth Axen will tell us about the fascinating world of protein structures and computational biology, and how his work of Bayesian modeler fits into that! Passionate about mathematics and statistics, Seth is finishing a PhD in bioinfo…
 
If you’ve studied at a business school, you probably didn’t attend any Bayesian stats course there. Well this isn’t like that in every business schools! Elea McDonnel Feit does integrate Bayesian methods into her teaching at the business school of Drexel University, in Philadelphia, US. Elea is an Assistant Professor of Marketing at Drexel, and in …
 
Cluster randomized trials are performed when an intervention must be delivered to a group of patients like when testing new nursing protocols on award or different means for cleaning beds on a ward. One type of cluster trials is called a stepped-wedge where every cluster in the study ultimately undergoes the intervention. How this works it is expla…
 
If, like me, you’ve been stuck in a 40 square-meter apartment for two months, you’re going to be pretty jealous of Avi Bryant. Indeed, Avi lives on Galiano Island, Canada, not very far from Vancouver, surrounded by forest, overlooking the Salish Sea. In this natural and beautiful — although slightly deer-infested — spot, Avi runs The Gradient Retre…
 
I bet you heard a lot about epidemiological compartmental models such as SIR in the last few months? But what are they exactly? And why are they so useful for epidemiological modeling? Elizaveta Semenova will tell you why in this episode, by walking us through the case study she recently wrote with the Stan team. She’ll also tell us how she used Ga…
 
Once upon a time, there was an enchanted book filled with hundreds of little plots, applied examples and linear regressions — the prettiest creature that was ever seen. Its authors were excessively fond of it, and its readers loved it even more. This magical book had a nice blue cover made for it, and everybody aptly called it « Regression and othe…
 
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