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Artwork for Learning Bayesian Statistics

Learning Bayesian Statistics

Alexandre Andorra
Bayesian Statistics
Bayesian Inference
Machine Learning
Causal Inference
Statistical Modeling
Pymc
Data Science
Gaussian Processes
Uncertainty Quantification
Bayesian Methods
Sports Analytics
Variational Inference
Stan
Probabilistic Programming
Bayesian Deep Learning
Artificial Intelligence
Covid-19
Gravitational Waves
Generative Models
Quantum Physics

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 learnin... more

PublishesWeeklyEpisodes196Founded7 years ago
Number of ListenersCategories
TechnologyScience

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Artwork for Learning Bayesian Statistics

Latest Episodes

Today's clip is from Episode 152 of the podcast, featuring Daniel Saunders. In this conversation, Daniel explores how Bayesian decision theory handles real-world risk aversion beyond the textbook maximum expected utility framework.

The key insight: ... more

Support & Resources

→ Support the show on Patreon

→ Bayesian Modeling Course (first 2 lessons free):

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work

Takeaways:

Q: Why is bridging dee... more

Today's clip is from Episode 154 of the podcast, with Thomas Pinder.

In this conversation, Thomas Pinder explains how Bayesian methods naturally lend themselves to causal modeling, and why that matters for real-world business decisions. The key insi... more

• Support & get perks!

• Bayesian Modeling course (first 2 lessons free)

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!

Takeaways:

Q: Why was GPJax created and how does it benefit ... more

Key Facts

Accepts Guests
Accepts Sponsors
Contact Information
Podcast Host
Number of Listeners
Find out how many people listen to this podcast per episode and each month.

Recent Guests

Andreas Munk
Founder of Evara, probabilistic programming researcher
Evara
Episode: #155 Probabilistic Programming for the Real World, with Andreas Munk
Cherian Koshy
VP at Kingslight; USA Today bestselling author of NeuroGiving
Kingslight
Episode: #153 The Neuroscience of Philanthropy, with Cherian Koshy
Daniel Saunders
Senior Data Scientist at PyMC Labs with a PhD in Philosophy
PyMC Labs
Episode: #152 A Bayesian decision theory workflow, with Daniel Saunders
Jonas Arruda
Mathematician and PhD researcher at the University of Bonn, key contributor to the BayesFlow Library.
University of Bonn
Episode: #151 Diffusion Models in Python, a Live Demo with Jonas Arruda
Alana Karen
Author and former lead at Google specializing in tech leadership and culture.
Author of The Hard Tech Era
Episode: #149 The Future of Work in Tech, with Alana Karen
Scott Berry
Statistician and co-founder of Berry Consultants, known for innovative clinical trial designs
Berry Consultants
Episode: #148 Adaptive Trials, Bayesian Thinking, and Learning from Data, with Scott Berry
Martin Ingram
Data scientist and Bayesian researcher with a PhD in applied Bayesian statistics.
Conarx
Episode: #147 Fast Approximate Inference without Convergence Worries, with Martin Ingram
Ethan Smith
PhD candidate at the University of Rochester focused on high-energy density physics.
University of Rochester
Episode: #146 Lasers, Planets, and Bayesian Inference, with Ethan Smith
Christoph Bamberg
Researcher in cognitive science and health psychology
University of Salzburg
Episode: #143 Transforming Nutrition Science with Bayesian Methods, with Christoph Bamberg

Host

Alex Andorra
Host of Learning Bayesian Statistics, an open-source enthusiast and core contributor to PyMC and ArviZ, with experience as a Senior Data Scientist.

Reviews

4.7 out of 5 stars from 236 ratings
  • Eric Trexler is the man

    I wanted to listen to this podcast but just can’t get past the annoying host .

    Apple Podcasts
    1
    ukfitness
    United Kingdom3 years ago
  • Amazing podcast

    Keep up the great work, I very much enjoy listening to this podcast :)

    Apple Podcasts
    5
    Funnymovie
    Germany3 years ago
  • Interesting Bayesian information

    Podcast Addict
    5
    bills
    4 years ago
  • The podcast for Bayesian statistics fans

    I first found this podcast searching for examples of applied Bayesian statistics. A year later, I still enjoy every show. It’s not only a great way to keep up to date with current developments in statistical programming but also a source of inspiration for how to apply statistical tools to any and all questions. A great add-on is that the podcast guests also talk about their failures, struggles and personal journeys, which makes statistics - a fearful and/or boring topic for many - more human ... more

    Apple Podcasts
    5
    Daniel Bern
    Germany4 years ago
  • Come for the great guests and their unique applications, stay for the novelty statistical rap music.

    Podchaser
    5
    Kristian Higgins
    4 years ago

Listeners Say

Key themes from listener reviews, highlighting what works and what could be improved about the show.

Some listeners have expressed dissatisfaction with presentation styles, noting areas for improvement, such as naturalness in audio delivery.
Listeners praise the podcast for its informative and engaging content, particularly highlighting the relevance of Bayesian statistics in practical applications.
Many appreciate the focus on learning from failures, making complex topics in statistics more relatable and approachable.
Overall, it is seen as a valuable resource for professionals and learners in statistics and data science.

Chart Rankings

How this podcast ranks in the Apple Podcasts, Spotify and YouTube charts.

Apple Podcasts
#105
Japan/Technology
Apple Podcasts
#178
Brazil/Technology
Apple Podcasts
#214
United Arab Emirates/Technology
Apple Podcasts
#222
Singapore/Technology
Apple Podcasts
#246
Philippines/Technology
Apple Podcasts
#249
South Korea/Technology

Talking Points

Recent interactions between the hosts and their guests.

#155 Probabilistic Programming for the Real World, with Andreas Munk
Q: How can practitioners start using PyProb today?
Begin by visiting the PyProb website and reviewing examples and documentation. Engage with maintainers, try out existing notebooks, and experiment with training simple inference networks within Python to understand amortized inference in probabilistic programs.
#155 Probabilistic Programming for the Real World, with Andreas Munk
Q: How does one balance model accuracy with communicating uncertainty to CFOs?
Rather than focusing solely on accuracy of point predictions, emphasize the distribution of possible outcomes, provide scenario-based decision aids, and use visuals to show how uncertainty affects risk and decisions, which often resonates more with business leadership.
#155 Probabilistic Programming for the Real World, with Andreas Munk
Q: What are the main challenges when applying these ideas in real enterprises?
The main challenges include explaining the value of probabilistic modeling to stakeholders who expect single numbers, integrating tools into existing workflows (like Excel), and developing intuitive interfaces and visualizations that convey uncertainty without overwhelming non-technical users.
#155 Probabilistic Programming for the Real World, with Andreas Munk
Q: Why build PyProb, and how does it help practitioners?
PyProb provides a practical platform to experiment with training neural networks as part of the inference process, allowing researchers to explore architectures and training strategies within a probabilistic programming language. It serves as a bridge between deep learning and probabilistic programming for faster, scalable inference.
#155 Probabilistic Programming for the Real World, with Andreas Munk
Q: What is inference compilation and how does it differ from traditional amortized inference?
Inference compilation trains neural networks to approximate the posterior so that, once trained, posterior samples can be drawn quickly for new observations. This differs from naive amortized inference by integrating the network training directly with the probabilistic program's execution, enabling on-the-fly construction of inference networks that respect the program's structure.

Audience Metrics

Listeners, social reach, demographics and more for this podcast.

Listeners per Episode
Gender Skew
Location
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Professions
Age Range
Household Income
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Frequently Asked Questions About Learning Bayesian Statistics

What is Learning Bayesian Statistics about and what kind of topics does it cover?

This podcast serves as a platform for exploring Bayesian inference with a focus on its practical applications across various fields. It features interviews with researchers and practitioners who share their experiences and methodologies in using Bayesian statistics to solve real-world problems, ranging from astrophysics to public health. A unique aspect is the emphasis on learning from failures, with guests often discussing challenges they faced and how they navigated these obstacles, fostering a culture of continuous learning. Ideal for data scientists, analysts, and anyone interested in understanding and applying Bayesian methods, the content is rich with examples, insights, and a community-driven ethos encouraging lifelong education in s... more

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Which podcasts are similar to Learning Bayesian Statistics?

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1. Machine Learning Street Talk (MLST)
2. Super Data Science: ML & AI Podcast with Jon Krohn
3. The Quanta Podcast
4. Latent Space: The AI Engineer Podcast
5. Sean Carroll's Mindscape: Science, Society, Philosophy, Culture, Arts, and Ideas

How many episodes of Learning Bayesian Statistics are there?

Learning Bayesian Statistics launched 7 years ago and published 196 episodes to date. You can find more information about this podcast including rankings, audience demographics and engagement in our podcast database.

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What guests have appeared on Learning Bayesian Statistics?

Recent guests on Learning Bayesian Statistics include:

1. Andreas Munk
2. Cherian Koshy
3. Daniel Saunders
4. Jonas Arruda
5. Alana Karen
6. Scott Berry
7. Martin Ingram
8. Ethan Smith

To view more recent guests and their details, simply upgrade your Rephonic account. You'll also get access to a typical guest profile to help you decide if the show is worth pitching.

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