Statistics public
[search 0]
×
Best Statistics podcasts we could find (updated August 2020)
Best Statistics podcasts we could find
Updated August 2020
Join millions of Player FM users today to get news and insights whenever you like, even when you're offline. Podcast smarter with the free podcast app that refuses to compromise. Let's play!
Join the world's best podcast app to manage your favorite shows online and play them offline on our Android and iOS apps. It's free and easy!
More
show episodes
 
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 practitioners of all fields about how they use Bayesian statistics, and how in turn YOU can apply these methods in your modeling workflow. Welcome to « Learning Bayesian Statistics », a fortnightly podcast on… Bayesian inference - the methods, the projects and the people who m ...
 
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 ...
 
Loading …
show series
 
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…
 
Do you know Turing? Of course you do! With Soss and Gen, it’s one of the blockbusters to do probabilistic programming in Julia. And in this episode Cameron Pfiffer will tell us all about it — how it came to life, how it fits into the probabilistic programming landscape, and what its main strengths and weaknesses are. Cameron did some Rust, some Pyt…
 
I hope you’re all safe! Some of you also asked me if I had set up a Patreon so that they could help support the show, and that’s why I’m sending this short special episode your way today. I had thought about that, but I wasn’t sure there was a demand for this. Apparently, there is one — at least a small one — so, first, I wanna thank you and say ho…
 
Fiona Lethbridge, Science Media Centre, gives a talk on the Science Media Centre and it's work. Fiona is a senior press officer at the Science Media Centre and has worked there since July 2012. She has a PhD in evolutionary biology from the University of Edinburgh. The Science Media Centre is an independent press office which opened in 2002 and bel…
 
How do you design a good experimental study? How do you even know that you’re asking a good research question? Moreover, how can you align funding and publishing incentives with the principles of an open source science? Let’s do another “big picture” episode to try and answer these questions! You know, these episodes that I want to do from time to …
 
Dr Fergus Cooper, Research Software Engineer, Oxford RSE Group, gives a talk for the department of Statistics on 5th June 2020. Following on from Graham Lee's talk on automated testing, we will use GitHub actions to automate the testing of a small Python project. We will: recap why this might be a good idea; walk through setting up a workflow on Gi…
 
Graham Lee, Research Software Engineer, Oxford RSE Group, gives talk for the department of Statistics on 22nd May 2020. Abstract: If we want reliable, reproducible simulations and data analysis software, we need to know that we have implemented our code correctly. Further, we need to be confident that changes we make to the code do not introduce un…
 
Nick Jewell, University of California, Berkeley School of Public Health, gives a talk for the departmental of Statistics on 28th May 2020. Abstract: The successful introduction of the intracellular bacterium Wolbachia into Aedes aegypti mosquitoes enables a practical approach for dengue prevention through release of Wolbachia-infected mosquitoes. W…
 
Radford M. Neal (University of Toronto), gives a talk for the department of Statistics. Hamiltonian Monte Carlo (HMC) is an attractive MCMC method for continuous distributions because it makes use of the gradient of the log probability density to propose points far from the current point, avoiding slow exploration by a random walk (RW). The Langevi…
 
Have you already encountered a model that you know is scientifically sound, but that MCMC just wouldn’t run? The model would take forever to run — if it ever ran — and you would be greeted with a lot of divergences in the end. Yeah, I know, my stress levels start raising too whenever I hear the word « divergences »… Well, you’ll be glad to hear the…
 
A librarian, a philosopher and a statistician walk into a bar — and they can’t find anybody to talk to; nobody seems to understand what they are talking about. Nobody? No! There is someone, and this someone is Will Kurt! Will Kurt is the author of ‘Bayesian Statistics the Fun Way’ and ‘Get Programming With Haskell’. Currently the lead Data Scientis…
 
This is it folks! This is the first of the special episodes I want to do from time to time, to expand our perspective and get inspired by what’s going on elsewhere. The guests will not come directly from the Bayesian world, but will still be related to science or programming. For the first episode of the kind, I had the chance to chat with Michael …
 
I bet you love penguins, right? The same goes for koalas, or puppies! But what about sharks? Well, my next guest loves sharks — she loves them so much that she works a lot with marine biologists, even though she’s a statistician! Vianey Leos Barajas is indeed a statistician primarily working in the areas of statistical ecology, time series modeling…
 
How is Julia doing? I’m talking about the programming language, of course! What does the probabilistic programming landscape in Julia look like? What are Julia’s distinctive features, and when would it be interesting to use it? To talk about that, I invited Chad Scherrer. Chad is a Senior Research Scientist at RelationalAI, a company that uses Arti…
 
Do you know Google Summer of Code? It’s a time of year when students can contribute to open-source software by developing and adding much needed functionalities to the open-source package of their choice. And Demetri Pananos did just that. He did it in 2019 with PyMC3, for which he developed the API for ordinary differential equations. In this epis…
 
I bet you already heard about hierarchical models, or multilevel models, or varying-effects models — yeah this type of models has a lot of names! Many people even turn to Bayesian tools to build _exactly_ these models. But what are they? How do you build and use a hierarchical model? What are the tricks and classical traps? And even more important:…
 
Providing a whirlwind tour of the quantitative analyses currently underway to understand the transmission and control of the novel coronavirus (2019-nCOV). Recorded on 31st January 2020. www.imperial.ac.uk -> mrc-global-infectious-disease-analysis -> News--wuhan-coronavirus LINK - https://tinyurl.com/mrc-global-infectious-diseaseBiology Preprint pa…
 
How do you handle your MCMC samples once your Bayesian model fit properly? Which diagnostics do you check to see if there was a computational problem? And isn’t that nice when you have beautiful and reliable plots to complement your analysis and better understand your model? I know what you think: plotting can be long and complicated in these cases…
 
Have you always wondered what dark matter is? Can we even see it — let alone measure it? And what would discover it imply for our understanding of the Universe? In this episode, we’ll take look at the cosmos with Maggie Lieu. She’ll tell us what research in astrophysics is made of, what model she worked on at the European Space Agency, and how Baye…
 
What is it like using Bayesian tools when you’re a software engineer or computer scientist? How do you apply these tools in the online ad industry? More generally, what is Bayesian thinking, philosophically? And is it really useful in every day life? Because, well you can’t fire up MCMC each time you need to make a quick decision under uncertainty……
 
You can’t study psychology up until your PhD and end-up doing very mathematical and computational data science at Google right? It’s too hard of a U-turn — some would even say it’s NUTS, just because they like bad puns… Well think again, because Junpeng Lao did just that! Before doing data science at Google, Junpeng was a cognitive psychology resea…
 
If you’re there, it’s probably because you’re interested in Bayesian inference, right? But don’t you feel lost sometimes when building a model? Or you ask yourself why what you’re trying to do is so damn hard… and you conclude that YOU are the problem, that YOU must be doing something wrong! Well, rest assured, as you’ll hear from Michael Betancour…
 
I have two questions for you: Are you a self-learner? Then how do you stay up to date? What should you focus on if you’re a beginner, or if you’re more advanced? And here is my second question: Are you working in biomedicine? And if you do, are you using Bayesian tools? Then how do you get your co-workers more used to posterior distributions than p…
 
What do neurodegenerative diseases, gerrymandering and ecological inference all have in common? Well, they can all be studied with Bayesian methods — and that’s exactly what Karin Knudson is doing. In this episode, Karin will share with us the vital and essential work she does to understand aspects of neurodegenerative diseases. She’ll also tell us…
 
How can you use Bayesian tools and optimize your models in industry? What are the best ways to communicate and visualize your models with non-technical and executive people? And what are the most common pitfalls? In this episode, Colin Carroll will tell us how he did all that in finance and the airline industry. He’ll also share with us what the fu…
 
When speaking about Bayesian statistics, we often hear about « probabilistic programming » — but what is it? Which languages and libraries allow you to program probabilistically? When is Stan, PyMC, Pyro or any other probabilistic programming language most appropriate for your project? And when should you even use Bayesian libraries instead of non-…
 
When are Bayesian methods most useful? Conversely, when should you NOT use them? How do you teach them? What are the most important skills to pick-up when learning Bayes? And what are the most difficult topics, the ones you should maybe save for later? In this episode, you’ll hear Chris Fonnesbeck answer these questions from the perspective of mari…
 
What do you get when you put a physicist, a biologist and a data scientist in the same body? Well, you’re about to find out… In this episode you’ll meet Osvaldo Martin. Osvaldo is a researcher at the National Scientific and Technical Research Council in Argentina and is notably the author of the book Bayesian Analysis with Python, whose second edit…
 
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? Well I'm just like you! When I started learning Bayesian methods, I really wished there were a podcast out there that could introduce me to the methods, the projects and the peop…
 
The Ninth annual Florence Nightingale Lecture, given by Professor Dame Janet Thornton, European Bioinformatics Institute, Cambridge. Held on Thursday 21st April 2016. Florence Nightingale was a celebrated nurse who served the British Army during the Crimean War. Her ground-breaking use of data visualisation turned a spotlight on the terrible hospit…
 
Loading …

Quick Reference Guide

Copyright 2020 | Sitemap | Privacy Policy | Terms of Service
Google login Twitter login Classic login