Learning Bayesian Statistics

📢 Episode 117 is live!   🎧https://lnkd.in/gyV7u3VY 😎This time, we’re diving into Bayesian experimental design with Alexandre Andorra and Desi R. Ivanova. It’s all about how to make smarter decisions!. Tune in for some great insights! 👀 Takeaways: 🔍 Experiment design is about gathering the right data in the best way. 📊 The goal? Maximize information. ❓ The most effective experiments reduce uncertainty. 💻 But… computational challenges can make it tough to apply in real life. ⚡ Amortized Bayesian inference helps speed things up. 🔄 Adaptive experiments are a lot more complex than static ones. #LearningBayesianStatistics #Bayesian #ExperimentalDesign

🚀 Squared Neural Families: A New Class of Tractable Density Models We’ve been exploring Squared Neural Families (SNEFY), and it’s a significant step forward in probabilistic modelling. 💡 ⚙️The idea is straightforward: take the 2-norm of a neural network, square it, and normalize it with a base measure. This results in a model that generalizes classical exponential families, while still providing closed-form normalizing constants in key cases. It’s a practical solution for tasks like estimation, conditional densities, and handling missing data. 📊 What makes SNEFY interesting:   🔸 It’s closed under conditioning, with tractable marginal distributions.   🔸 It works well with modern neural network architectures.   🔸 It shows solid empirical results across different datasets. Whether you’re working on anomaly detection 🚨 or generative modelling 🤖, SNEFY offers a robust approach. 🔗 Dive into the details here : https://lnkd.in/gwgHdsiG Let’s keep advancing probabilistic models! 🔥💻  #LearningBayesianStatistics #Research

Getting ready for PyData NYC 😍 We have two live shows coming up on November 7 & 8! 🎤 If you wanna be part of the live experience, join the Q&As, and connect with the speakers and Alexandre Andorra, get your ticket at https://lnkd.in/eFwBHT6x We can’t wait to see you there!

Super excited to see the Learning Bayesian Statistics podcast get a shoutout on Andrew Gelman’s blog! 🙌 Big thanks to Bob Carpenter for mentioning us in his post about defining statistical models in JAX. It’s awesome to be part of the conversation around Bayesian stats and modern tools. Definitely worth checking out! Here’s the link 👉 https://lnkd.in/gdm9xuWu #LearningBayesianStatistics #BayesianStats

📅 We’re bringing 𝗟𝗕𝗦 to 𝗣𝘆𝗗𝗮𝘁𝗮 𝗡𝗬𝗖!  on Nov 7 & 8! 🎤 Be part of the live experience, join the Q&As, connect with the speakers and host Alexandre Andorra afterwards 🎟️ Tickets are available on the PyData NYC ✅ Get your tickets here: https://lnkd.in/geS2_ygU #LiveShow #LearningBayesianStatistics

📢 Episode 116 is now available! 🎧 https://lnkd.in/gZHX7evP In this episode, Alexandre Andorra and Ravi Ramineni dive deep into the world of football analytics. Key takeaways: ⚽ Building athlete management systems and scouting platforms are critical for smart training, injury prevention, and player signings. 🔍 Avoiding false positives in player evaluations requires accurate data analysis. ☁️ Transitioning from on-premises SQL servers to cloud-based systems is revolutionizing football analytics. 📊 Analytics aid in decision-making and help reduce biases, influencing everything from player recruitment to a decrease in long-range shots.  🤝 Collaboration between analysts and decision-makers is essential for success. 📈 Predictive metrics and modelling are vital for tracking young players' career progression.  #LearningBayesianStatistics #Analytics

📢Episode 115 is now available! 🎧https://lnkd.in/gQrtFtsy 👉 In Episode 115, Alexandre Andorra sits down with Nathaniel Haines to explore how state space models and time series approaches are useful. We dive into why and how techniques like Bayesian model stacking are helping overcome the challenges of working with limited data. 🎲 Nathaniel also introduces us to BayesBlend, a tool from Ledger Investing that simplifies combining model predictions and helps navigate the tricky world of model comparison. We talk about blending predictions, why out-of-sample performance metrics matter, and how simulation-based calibration (SBC) plays a role in getting better, more trustworthy results. ✅ If you’re interested in how Bayesian and classical methods shape industries like insurance, this one's for you!

🌌 Bayesian Inference with Preconditioned Monte Carlo! Preconditioned Monte Carlo (PMC) is a powerful new approach, making waves in high-dimensional Bayesian inference. What makes PMC so exciting? It delivers results 25 to 50 times faster than traditional methods, which is a significant advantage for complex tasks like gravitational wave analysis 🌊 or searching for primordial features in cosmology 🌠. ✨ Highlights of PMC:   🔄 Transforms complex probability distributions into simpler forms, enhancing sampling efficiency.   📈 Adapts dynamically during the process, becoming more efficient with each step.   🖥️ Scales effortlessly, whether running on a single machine or across thousands of CPUs.  The Python package, pocoMC, is open-source and integrates smoothly into existing workflows. It’s available for anyone ready to explore its capabilities. 🤓 ✨ Dive in and learn more: https://lnkd.in/g6fR3nqc Let's continue pushing the boundaries of Bayesian analysis! 🌍 #LearningBayesianStatistics #BayesianInference

📢 Episode 114 is now available! 🎧 https://lnkd.in/gAihprJN ⚾️ Tune in as Alexandre Andorra chats with Jacob Buffa about his journey in baseball science and the blend of sports and data. 🔍 Jacob discusses how education helps athletes understand nutrition's impact and how Bayesian statistics can help analyze performance and injury risks. 🤓 He highlights the need to tailor conditioning to each athlete and communicate complex concepts like Bayesian analysis effectively. 🚀 The episode also explores new trends in baseball science, such as biomechanics and computer vision, to enhance performance and prevent injuries and much more... #LearningBayesianStatistics #SportsPerformance

🎤 Get Ready: LBS is Going LIVE for the First Time Ever at 𝐒𝐭𝐚𝐧𝐂𝐨𝐧! 🎉 🔥 Two LBS Live Shows are coming soon! 😉 Sept 10: Charles Margossian, Steve Bronder, and Brian Ward kick things off with a deep dive into 𝐒𝐭𝐚𝐧 journey and its exciting future! 🧬 Sept 11: Chris Wymant and Elizaveta Semenova bring computational biology and epidemiology to life making science seriously cool! ✅ Don’t miss out mark your calendars! 👉 Make sure you’re in on the action! Sign up for 𝐒𝐭𝐚𝐧𝐂𝐨𝐧 today! 🔗 https://lnkd.in/ePg_e67g #LearnngBayesianStatistics #StanCon2024