Top Tweets for #BayesianStatistics
Production MMM breaks when influencers aren't interchangeable, campaigns shift, and channels enter mid-series.
Part III of our Nürnberger case study tackles all three. 27%+ CPL reduction:
https://t.co/wWKHSrzdeo
#MarketingMixModeling #BayesianStatistics #PyMC
🎓 Fully Funded PhD in Biostochastics (Sweden 🇸🇪)
💶 Fully funded 4-year PhD with competitive doctoral salary progression + outstanding research environment and benefits
✅ Passionate about #ComputationalBiology #DeepLearning #BayesianStatistics 🧬🧠📊
✅ Highly recommend this advanced interdisciplinary #fullyfunded #PhDPosition within the Department of Microbiology, Tumor and Cell Biology at @karolinskainst 🇸🇪
📌 This #phdproject focuses on developing flexible yet interpretable statistical and deep learning models for complex biological systems
You’ll work on:
🔷 Developing new modeling frameworks that bridge mechanistic and data-driven approaches
🔷 Designing and implementing deep learning and generative modeling methods for biological systems
🔷 Working across ecology, phylogenetics, and protein generative modeling applications
🔷 Applying advanced Bayesian inference, stochastic processes, Gaussian Processes, and latent-variable models
🔷 Conducting computational experiments and collaborating with experimental biology labs
🔷 Publishing cutting-edge interdisciplinary research at the intersection of statistics, AI, and biology
🌍 Contribute to next-generation computational biology by developing interpretable and scalable models that advance understanding of complex living systems.
✅ Work with Dr. @BenjMurrell and the Biostochastics research environment at Karolinska Institutet
⏰ 𝗗𝗲𝗮𝗱𝗹𝗶𝗻𝗲: 𝟭𝟱𝘁𝗵 𝗝𝘂𝗻𝗲, 𝟮𝟬𝟮𝟲
👉 Full details & apply here:
🔗 https://t.co/hPF0HJChrc
📩 Want more like this?
➕ Follow @PhdScanner and join WhatsApp for updates:
https://t.co/d89Rl8tH7S
🌐 Visit: https://t.co/yET6DsYARx
#fullyfundedPhD #PhDposition #KarolinskaInstitutet #Sweden #Biostochastics #ComputationalBiology #DeepLearning #GenerativeModels #BayesianStatistics #MachineLearning #Bioinformatics #ResearchOpportunity
@phdhardtalk
♻️ Share with someone applying this cycle
When your MMM ignores funnel dynamics and budget caps, upper-funnel channels look like they underperform. They don't.
Part II of our Nürnberger case study shows how a censored-likelihood Bayesian MMM fixes it https://t.co/DfDecIC0yj
#MMM #BayesianStatistics #PyMC
@Wegmans needed to quantify cannibalization risk before opening new stores. We built a Bayesian system that answers in minutes, not weeks.
Full technical walkthrough: https://t.co/wkMSBjIFKm
#RetailAnalytics #BayesianStatistics #PyMC
How can platform trials learn from sparse subgroup data without blurring real biological differences? Our paper explores Bayesian hierarchical borrowing through the I-SPY 2/neratinib example.
➡️ https://t.co/dKwb2MrgjT
#SporeDataResearch #BayesianStatistics #ClinicalTrials
📢 Must-Read in #Forecasting
📖 Bayesian LASSO with Categorical Predictors: Coding Strategies, Uncertainty Quantification, and Healthcare Applications
✍️ Xi Lu et. al.
🔗 https://t.co/vae7ILChf8
#BayesianStatistics #MachineLearning #HealthcareAnalytics
"How many people did COVID kill in the UK?" is the wrong question.
The right one is counterfactual: how many would have died without it?
You can't observe that world. You have to model it.
#BayesianStatistics #PyMC #CausalInference
How can Bayesian methods help us learn more from Studies Within A Trial (SWATs)? Find out here where we explore how Bayesian methods and ACCEPT analysis help us get more meaningful insights from SWATs: https://t.co/As9SeOqPpv #Trials #TrialMethodology #SWAT #BayesianStatistics
📝Improper Priors via Expectation Measures
👥by Peter Harremoës
#BayesianStatistics; #ExpectationMeasure; #ImproperPriorDistribution; #ExpectedValue; #PointProcess; #PoissonPointProcess; #sFiniteMeasure; #PosteriorDistribution; #StatisticalModel
📖 https://t.co/AprZLKxO20
There are still seats available for the SDM with BART methods
📅 1–3 July
Learn how to build, evaluate, and interpret #SDMs in R using BART methods & compare them to other #machinelearning approaches in ecology
https://t.co/CmcTGoZJxm
#Rstats #BayesianStatistics
NITheCS & SU Dept. of Statistics Seminar:
‘Bayesian study on tumour burden using functional uniform priors in nonlinear mixed-effects models’ by Mia Meyer
📅 Fri, 24 Apr
⏰ 13h10–14h10
🔗 https://t.co/H2MxjBUWGe
#BayesianStatistics #Biostatistics #NonlinearModels #CancerResearch
Featured case study from the community: a two-pillar Bayesian CLV framework for a nonprofit, built on pymc-marketing.
1M+ records, MCMC vs. MAP tradeoffs, and a full Databricks deployment walkthrough
Read the blog here: https://t.co/tT73QWo9sP
#PyMC #BayesianStatistics #CLV
We're bringing Bayesian modeling to London, live and in person!
June 8-10: Learn to build probabilistic models in #PyMC, guided by its creators.
Small cohort, hands-on, walk out with working code. Seats are limited.
👉 https://t.co/34awl0kGIb
#BayesianStatistics
🚀 DeepMind is hiring Research Scientist
🌇 London, UK
💪 #AI #Machine Learning #ai #machinelearning #informationtheory #bayesianstatistics #multi-agentsystems
#tech #softwareengineer #jobs
https://t.co/lKX8yZtqyd
Live online course on Bayesian statistical modelling with Stan & brms, taught by Mark Andrews, Senior Lecturer & experienced instructor in applied Bayesian methods
5–7 May 2026
Details: https://t.co/yncySc9BzM
#PhDStudents #BayesianStatistics #Stan #RStats #DataScience
Live online course on scalable Bayesian modelling using INLA, taught by Virgilio Gómez-Rubio, author of Bayesian Inference with INLA and leading contributor to Bayesian & spatial modelling in R
4–8 May
https://t.co/BtNSRP2zzb
#BayesianStatistics #INLA #RStats #SpatialStatistics
Live online course on stable isotope mixing models using SIBER, SIAR, & MixSIAR, taught by Andrew Parnell, Hamilton Professor of Statistics with extensive expertise in Bayesian modelling.
27–30 Apr
https://t.co/CWks3bAhr0
#StableIsotopes #BayesianStatistics #Ecology #RStats
TrendToKnow AI: Parametric Mean-Field empirical Bayes in high-dimensional linear regression
👥 Seunghyun Lee & Nabarun Deb
#AIResearch #MachineLearning #BayesianStatistics #HighDimensionalData
Provided by TrendToKnow AI
🔗 https://t.co/LKJEWWPS4X
Introduction to Bayesian Inference in Practice 📊✨
An opportunity to engage with Bayesian thinking and its applications in real research contexts.
More: https://t.co/2kTNvzyrGH
#BayesianStatistics #ResearchMethods
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