“‘Bayes’ Tag”,2018-12-12 (cognitive bias)⁠:

Bibliography for tag statistics/bayes, most recent first: 3 related tags, 242 annotations, & 34 links (parent).

• See Also
• Gwern
  • “Acne: a Good Quantified Self Topic”, Gwern2019
  • “Statistical Notes”, Gwern2014
  • “Everything Is Correlated”, Gwern2014
  • “Calculating The Gaussian Expected Maximum”, Gwern2016
  • “How Often Does Correlation=Causality?”, Gwern2014
  • “One Man’s Modus Ponens”, Gwern2012
  • “Evolution As Backstop for Reinforcement Learning”, Gwern2018
  • “Regression To The Mean Fallacies”, Gwern2021
  • “Self-Blinded Mineral Water Taste Test”, Gwern2017
  • “Banner Ads Considered Harmful”, Gwern2017
  • “Magnesium Self-Experiments”, Gwern2013
  • “The Most ‘Abandoned’ Books on GoodReads”, Gwern2019
  • “Why Correlation Usually ≠ Causation”, Gwern2014
  • “How Should We Critique Research?”, Gwern2019
  • “Catnip Immunity and Alternatives”, Gwern2015
  • “Prediction Markets”, Gwern2009
  • “The Explore-Exploit Dilemma in Media Consumption”, Gwern2016
  • “Embryo Editing for Intelligence”, Gwern2016
  • “Frank P. Ramsey Bibliography”, Gwern2019
  • “Nootropics”, Gwern2010
  • “World Catnip Surveys”, Gwern2015
  • “Life Extension Cost-Benefits”, Gwern2015
  • “Resorting Media Ratings”, Gwern2015
  • “Bacopa Quasi-Experiment”, Gwern2014
  • “ZMA Sleep Experiment”, Gwern2017
  • “Zeo Sleep Self-Experiments”, Gwern2010
  • “When Should I Check The Mail?”, Gwern2015
  • “Biased Information As Anti-Information”, Gwern2012
  • “Death Note: L, Anonymity & Eluding Entropy”, Gwern2011
  • “Potassium Sleep Experiments”, Gwern2012
  • “Caffeine Wakeup Experiment”, Gwern2013
  • “Vitamin D Sleep Experiments”, Gwern2012
  • “Candy Japan’s New Box A/B Test”, Gwern2016
  • “Bitter Melon for Blood Glucose”, Gwern2015
  • “Who Wrote The Death Note Script?”, Gwern2009
  • “Charity Is Not about Helping”, Gwern2011
  • “2012 Election Predictions”, Gwern2012
• Links
  • “Towards a Law of Iterated Expectations for Heuristic Estimators”, Christiano et al 2024
  • “The Economic Way of Thinking in a Pandemic”, Tabarrok2024
  • “Safety Alignment Should Be Made More Than Just a Few Tokens Deep”, Qi et al 2024
  • “The Matrix: A Bayesian Learning Model for LLMs”, Dalal & Misra2024
  • “Deep De Finetti: Recovering Topic Distributions from Large Language Models”, Zhang et al 2023
  • “Bayesian Regression Markets”, Falconer et al 2023
  • “Model Merging by Uncertainty-Based Gradient Matching”, Daheim et al 2023
  • “How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?”, Wu et al 2023
  • “Dynamical versus Bayesian Phase Transitions in a Toy Model of Superposition”, Chen et al 2023
  • “Bayesian Flow Networks”, Graves et al 2023
  • “Supervised Pretraining Can Learn In-Context Reinforcement Learning”, Lee et al 2023
  • “Pretraining Task Diversity and the Emergence of Non-Bayesian In-Context Learning for Regression”, Raventós et al 2023
  • “Posterior Sampling for Multi-Agent Reinforcement Learning: Solving Extensive Games With Imperfect Information”, Zhou et al 2023
  • “Fundamental Limitations of Alignment in Large Language Models”, Wolf et al 2023
  • “Emergence of Belief-Like Representations through Reinforcement Learning”, Hennig et al 2023
  • “Modern Bayesian Experimental Design”, Rainforth et al 2023
  • “Unifying Approaches in Active Learning and Active Sampling via Fisher Information and Information-Theoretic Quantities”, Kirsch & Gal2023
  • “Mortality Postponement and Compression at Older Ages in Human Cohorts”, McCarthy & Wang2023
  • “How Do Psychology Researchers Interpret the Results of Multiple Replication Studies?”, Akker et al 2023
  • “Robust Bayesian Meta-Analysis: Addressing Publication Bias With Model-Averaging”, Maier et al 2023
  • “Robust Bayesian Meta-Analysis: Model-Averaging across Complementary Publication Bias Adjustment Methods”, Bartoš et al 2023
  • “AlphaZe∗∗: AlphaZero-Like Baselines for Imperfect Information Games Are Surprisingly Strong”, Blüml et al 2023
  • “What Learning Algorithm Is In-Context Learning? Investigations With Linear Models”, Akyürek et al 2022
  • “Laplace’s Demon in Biology: Models of Evolutionary Prediction”, Gompert et al 2022
  • “Are Most Published Criminological Research Findings Wrong? Taking Stock of Criminological Research Using a Bayesian Simulation Approach”, Niemeyer et al 2022
  • “A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning”, Dann et al 2022
  • “Zeus: Understanding and Optimizing GPU Energy Consumption of DNN Training”, You et al 2022
  • “Language Model Cascades”, Dohan et al 2022
  • “Language Models (Mostly) Know What They Know”, Kadavath et al 2022
  • “Offline RL Policies Should Be Trained to Be Adaptive”, Ghosh et al 2022
  • “Greedy Bayesian Posterior Approximation With Deep Ensembles”, Tiulpin & Blaschko2022
  • “Teaching Models to Express Their Uncertainty in Words”, Lin et al 2022
  • “RL With KL Penalties Is Better Viewed As Bayesian Inference”, Korbak et al 2022
  • “Fast and Accurate Bayesian Polygenic Risk Modeling With Variational Inference”, Zabad et al 2022
  • “On-The-Fly Strategy Adaptation for Ad-Hoc Agent Coordination”, Zand et al 2022
  • “The InterModel Vigorish (IMV): A Flexible and Portable Approach for Quantifying Predictive Accuracy With Binary Outcomes”, Domingue et al 2022
  • “PFNs: Transformers Can Do Bayesian Inference”, Müller et al 2021
  • “The Science of Visual Data Communication: What Works”, Franconeri et al 2021
  • “How to Learn and Represent Abstractions: An Investigation Using Symbolic Alchemy”, AlKhamissi et al 2021
  • “An Experimental Design Perspective on Model-Based Reinforcement Learning”, Mehta et al 2021
  • “Prior Knowledge Elicitation: The Past, Present, and Future”, Mikkola et al 2021
  • “Improving GWAS Discovery and Genomic Prediction Accuracy in Biobank Data”, Orliac et al 2021
  • “An Explanation of In-Context Learning As Implicit Bayesian Inference”, Xie et al 2021
  • “Unifying Individual Differences in Personality, Predictability and Plasticity: A Practical Guide”, O’Dea et al 2021
  • “A Confirmation Bias in Perceptual Decision-Making due to Hierarchical Approximate Inference”, Lange et al 2021
  • “MegaLMM: Mega-Scale Linear Mixed Models for Genomic Predictions With Thousands of Traits”, Runcie et al 2021
  • “Why Generalization in RL Is Difficult: Epistemic POMDPs and Implicit Partial Observability”, Ghosh et al 2021
  • “The Bayesian Learning Rule”, Khan & Rue2021
  • “No Need to Choose: Robust Bayesian Meta-Analysis With Competing Publication Bias Adjustment Methods”, Bartoš et al 2021
  • “Maternal Judgments of Child Numeracy and Reading Ability Predict Gains in Academic Achievement and Interest”, Parker et al 2021
  • “Genetic Sensitivity Analysis: Adjusting for Genetic Confounding in Epidemiological Associations”, Pingault et al 2021
  • “What Are Bayesian Neural Network Posteriors Really Like?”, Izmailov et al 2021
  • “Bayesian Optimization Is Superior to Random Search for Machine Learning Hyperparameter Tuning: Analysis of the Black-Box Optimization Challenge 2020”, Turner et al 2021
  • “Maximal Positive Controls: A Method for Estimating the Largest Plausible Effect Size”, Hilgard2021
  • “Informational Herding, Optimal Experimentation, and Contrarianism”, Smith et al 2021
  • “Image Completion via Inference in Deep Generative Models”, Harvey et al 2021
  • “The Statistical Properties of RCTs and a Proposal for Shrinkage”, Zwet et al 2020
  • “Hot under the Collar: A Latent Measure of Interstate Hostility”, Terechshenko2020
  • “What Matters More for Entrepreneurship Success? A Meta-Analysis Comparing General Mental Ability and Emotional Intelligence in Entrepreneurial Settings”, Allen et al 2020
  • “Bayesian Workflow”, Gelman et al 2020
  • “From Probability to Consilience: How Explanatory Values Implement Bayesian Reasoning”, Wojtowicz & DeDeo2020
  • “Meta-Trained Agents Implement Bayes-Optimal Agents”, Mikulik et al 2020
  • “Learning Not to Learn: Nature versus Nurture in Silico”, Lange & Sprekeler2020
  • “Searching for the Backfire Effect: Measurement and Design Considerations”, Swire-Thompson et al 2020
  • “A Bayesian Approach to the Simulation Argument”, Kipping2020
  • “Is SGD a Bayesian Sampler? Well, Almost”, Mingard et al 2020
  • “Laplace’s Theories of Cognitive Illusions, Heuristics and Biases”, Miller & Gelman2020
  • “Exploring Bayesian Optimization: Breaking Bayesian Optimization into Small, Sizeable Chunks”, Agnihotri & Batra2020
  • “Bayesian REX: Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences”, Brown et al 2020
  • “Bayesian Deep Learning and a Probabilistic Perspective of Generalization”, Wilson & Izmailov2020
  • “Why the Increasing Use of Complex Causal Models Is a Problem: On the Danger Sophisticated Theoretical Narratives Pose to Truth”, Saylors & Trafimow2020
  • “Improved Polygenic Prediction by Bayesian Multiple Regression on Summary Statistics”, Lloyd-Jones et al 2019
  • “The Propensity for Aggressive Behavior and Lifetime Incarceration Risk: A Test for Gene-Environment Interaction (G × E) Using Whole-Genome Data”, Barnes et al 2019
  • “Approximate Inference in Discrete Distributions With Monte Carlo Tree Search and Value Functions”, Buesing et al 2019
  • “Bayesian Parameter Estimation Using Conditional Variational Autoencoders for Gravitational-Wave Astronomy”, Gabbard et al 2019
  • “New Paradigms in the Psychology of Reasoning”, Oaksford & Chater2019
  • “Estimating Distributional Models With Brms: Additive Distributional Models”, Bürkner2019
  • “Dirichlet-Hawkes Processes With Applications to Clustering Continuous-Time Document Streams”, Du et al 2019
  • “Allocation to Groups: Examples of Lord’s Paradox”, Wright2019
  • “Evolutionary Implementation of Bayesian Computations”, Czégel et al 2019
  • “Reinforcement Learning, Fast and Slow”, Botvinick et al 2019
  • “Meta Reinforcement Learning As Task Inference”, Humplik et al 2019
  • “Structural Equation Models As Computation Graphs”, Kesteren & Oberski2019
  • “Meta-Learning of Sequential Strategies”, Ortega et al 2019
  • “Meta-Learners’ Learning Dynamics Are unlike Learners’”, Rabinowitz2019
  • “Is the FDA Too Conservative or Too Aggressive?: A Bayesian Decision Analysis of Clinical Trial Design”, Isakov et al 2019
  • “Approximate Bayesian Computation [Review]”, Beaumont2019
  • “Bayesian Statistics in Sociology: Past, Present, and Future”, Lynch & Bartlett2019
  • “Accounting Theory As a Bayesian Discipline”, Johnstone2018
  • “The Bayesian Superorganism III: Externalized Memories Facilitate Distributed Sampling”, Hunt et al 2018
  • “Exploration in the Wild”, Schulz et al 2018
  • “Bayesian Layers: A Module for Neural Network Uncertainty”, Tran et al 2018
  • “The Bayesian Superorganism I: Collective Probability Estimation”, Hunt et al 2018
  • “Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning”, Foerster et al 2018
  • “Computational Mechanisms of Curiosity and Goal-Directed Exploration”, Schwartenbeck et al 2018
  • “Accurate Uncertainties for Deep Learning Using Calibrated Regression”, Kuleshov et al 2018
  • “The Alignment Problem for Bayesian History-Based Reinforcement Learners”, Everitt & Hutter2018
  • “Mining Gold from Implicit Models to Improve Likelihood-Free Inference”, Brehmer et al 2018
  • “Deep Learning Generalizes Because the Parameter-Function Map Is Biased towards Simple Functions”, Valle-Pérez et al 2018
  • “Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling”, Riquelme et al 2018
  • “Posterior Sampling for Large Scale Reinforcement Learning”, Theocharous et al 2017
  • “Implicit Causal Models for Genome-Wide Association Studies”, Tran & Blei2017
  • “Analogical-Based Bayesian Optimization”, Le et al 2017
  • “DropoutDAgger: A Bayesian Approach to Safe Imitation Learning”, Menda et al 2017
  • “A Rational Choice Framework for Collective Behavior”, Krafft2017
  • “Better Decision Making in Drug Development Through Adoption of Formal Prior Elicitation”, Dallow et al 2017
  • “The Prior Can Generally Only Be Understood in the Context of the Likelihood”, Gelman et al 2017
  • “Statistical Correction of the Winner’s Curse Explains Replication Variability in Quantitative Trait Genome-Wide Association Studies”, Palmer & Pe’er2017
  • “A Tutorial on Thompson Sampling”, Russo et al 2017
  • “Structured Bayesian Pruning via Log-Normal Multiplicative Noise”, Neklyudov et al 2017
  • “PBO: Preferential Bayesian Optimization”, Gonzalez et al 2017
  • “Bayesian Recurrent Neural Networks”, Fortunato et al 2017
  • “Black-Box Data-Efficient Policy Search for Robotics”, Chatzilygeroudis et al 2017
  • “The Kelly Coin-Flipping Game: Exact Solutions”, Gwern et al 2017
  • “A Conceptual Introduction to Hamiltonian Monte Carlo”, Betancourt2017
  • “Simple and Scalable Predictive Uncertainty Estimation Using Deep Ensembles”, Lakshminarayanan et al 2016
  • “Bayesian Reinforcement Learning: A Survey”, Ghavamzadeh et al 2016
  • “Human Collective Intelligence As Distributed Bayesian Inference”, Krafft et al 2016
  • “Universal Darwinism As a Process of Bayesian Inference”, Campbell2016
  • “PHENIX: A Multiple-Phenotype Imputation Method for Genetic Studies”, Dahl et al 2016
  • “Probabilistic Integration: A Role in Statistical Computation?”, Briol et al 2015
  • “Practical Probabilistic Programming With Monads”, Ścibior et al 2015
  • “Don’t Fight the Power (analysis)”, Westfall2015
  • “Bayesian Dark Knowledge”, Korattikara et al 2015
  • “Dropout As a Bayesian Approximation: Representing Model Uncertainty in Deep Learning”, Gal & Ghahramani2015
  • “Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-Armed Bandit Problem With Multiple Plays”, Komiyama et al 2015
  • “Probabilistic Line Searches for Stochastic Optimization”, Mahsereci & Hennig2015
  • “Gaussian Processes for Data-Efficient Learning in Robotics and Control”, Deisenroth et al 2015
  • “LDpred: Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores”, Vilhjálmsson et al 2015
  • “Simultaneous Discovery, Estimation and Prediction Analysis of Complex Traits Using a Bayesian Mixture Model”, Moser et al 2014
  • “Predictive Distributions for Between-Study Heterogeneity and Simple Methods for Their Application in Bayesian Meta-Analysis”, Turner et al 2014
  • “One Hundred Years of Statistical Developments in Animal Breeding”, Gianola & Rosa2014
  • “Thompson Sampling With the Online Bootstrap”, Eckles & Kaptein2014
  • “Freeze-Thaw Bayesian Optimization”, Swersky et al 2014
  • “Search for the Wreckage of Air France Flight AF 447”, Stone et al 2014
  • “Bayesian Model Selection: The Steepest Mountain to Climb”, Tenan et al 2014
  • “Bayesian Inferences about the Self (and Others): a Review”, Moutoussis et al 2014
  • “Auto-Encoding Variational Bayes”, Kingma & Welling2013
  • “Machine Teaching for Bayesian Learners in the Exponential Family”, Zhu2013
  • “(More) Efficient Reinforcement Learning via Posterior Sampling”, Osband et al 2013
  • “Model-Based Bayesian Exploration”, Dearden et al 2013
  • “Understanding Predictive Information Criteria for Bayesian Models”, Gelman2013
  • “(More) Efficient Reinforcement Learning via Posterior Sampling [PSRL]”, Osband2013
  • “Deep Gaussian Processes”, Damianou & Lawrence2012
  • “A Widely Applicable Bayesian Information Criterion”, Watanabe2012
  • “Bayesian Estimation Supersedes the t-Test”, Kruschke2012
  • “Practical Bayesian Optimization of Machine Learning Algorithms”, Snoek et al 2012
  • “Learning Is Planning: near Bayes-Optimal Reinforcement Learning via Monte-Carlo Tree Search”, Asmuth & Littman2012
  • “Learning Performance of Prediction Markets With Kelly Bettors”, Beygelzimer et al 2012
  • “Bayesian Active Learning for Classification and Preference Learning”, Houlsby et al 2011
  • “Estimating the Evidence—A Review”, Friel & Wyse2011
  • “PILCO: A Model-Based and Data-Efficient Approach to Policy Search”, Deisenroth & Rasmussen2011
  • “Planning to Be Surprised: Optimal Bayesian Exploration in Dynamic Environments”, Sun et al 2011
  • “An Empirical Evaluation of Thompson Sampling”, Chapelle & Li2011
  • “Mice: Multivariate Imputation by Chained Equations in R”, Buuren & Groothuis-Oudshoorn2011
  • “Lack of Confidence in Approximate Bayesian Computation Model Choice”, Robert2011
  • “Bayesian Data Analysis”, Kruschke2010
  • “Darwin, Galton and the Statistical Enlightenment”, Stigler2010b
  • “Monte-Carlo Planning in Large POMDPs”, Silver & Veness2010
  • “Case Studies in Bayesian Computation Using INLA”, Martino & Rue2010
  • “Are Birds Smarter Than Mathematicians? Pigeons (Columba Livia) Perform Optimally on a Version of the Monty Hall Dilemma”, Herbranson & Schroeder2010
  • “A Monte Carlo AIXI Approximation”, Veness et al 2009
  • “Observed Universality of Phase Transitions in High-Dimensional Geometry, With Implications for Modern Data Analysis and Signal Processing”, Donoho & Tanner2009
  • “When Superstars Flop: Public Status and Choking Under Pressure in International Soccer Penalty Shootouts”, Jordet2009
  • “Models for Potentially Biased Evidence in Meta-Analysis Using Empirically Based Priors”, Welton et al 2008
  • “Optimal Approximation of Signal Priors”, Hyvarinen2008
  • “Verbal Probability Expressions In National Intelligence Estimates: A Comprehensive Analysis Of Trends From The Fifties Through Post-9/11”, Kesselman2008
  • “On Universal Prediction and Bayesian Confirmation”, Hutter2007
  • “Introduction History of Drosophila Subobscura in the New World: a Microsatellite-Based Survey Using ABC Methods”, Pascual et al 2007
  • “Experiments on Partisanship and Public Opinion: Party Cues, False Beliefs, and Bayesian Updating”, Bullock2007
  • “A Free Energy Principle for the Brain”, Friston et al 2006
  • “The Optimizer’s Curse: Skepticism and Postdecision Surprise in Decision Analysis”, Smith & Winkler2006
  • “Three Statistical Paradoxes in the Interpretation of Group Differences: Illustrated With Medical School Admission and Licensing Data”, Wainer & Brown2006
  • “Estimation of Non-Normalized Statistical Models by Score Matching”, Hyvarinen2005
  • “The Bayesian Brain: the Role of Uncertainty in Neural Coding and Computation”, Knill & Pouget2004
  • “Bayesian Informal Logic and Fallacy”, Korb2004
  • “Two Statistical Paradoxes in the Interpretation of Group Differences: Illustrated With Medical School Admission and Licensing Data”, Wainer & Brown2004
  • “Bayesian Computation: a Statistical Revolution”, Brooks2003
  • “Bayesian Adaptive Exploration”, Loredo & Chernoff2003
  • “Constructing a Logic of Plausible Inference: A Guide to Cox’s Theorem”, Horn2003
  • “Approximate Bayesian Computation in Population Genetics”, Beaumont et al 2002
  • “Simplifying Likelihood Ratios”, McGee2002
  • “A Bayesian Framework for Reinforcement Learning”, Strens2000
  • “Kelley’s Paradox”, Wainer2000
  • “Classical Multilevel and Bayesian Approaches to Population Size Estimation Using Multiple Lists”, Fienberg et al 1999
  • “A Conversation With I. Richard Savage (with the Assistance of Bruce Spencer)”, Sampson1999
  • “On the Optimality of the Simple Bayesian Classifier under Zero-One Loss”, Domingos & Pazzani1997
  • “Statistical Issues in the Analysis of Data Gathered in the New Designs”, Kadane & Seidenfeld1996
  • “Bayesian Estimation and the Kalman Filter”, Barker et al 1995
  • “Is There Sufficient Historical Evidence to Establish the Resurrection of Jesus?”, Cavin1995
  • “The Relevance of Group Membership for Personnel Selection: A Demonstration Using Bayes’ Theorem”, Miller1994
  • “Perceptual-Cognitive Universals As Reflections of the World”, Shepard1994
  • “Subjective Probability”, Wright & Ayton1994
  • “The Influence of Prior Beliefs on Scientific Judgments of Evidence Quality”, Koehler1993
  • “Statistical Theory of Learning Curves under Entropic Loss Criterion”, Amari & Murata1993
  • “Some Formulas for Use With Bayesian Ability Estimates”, Mislevy1993
  • “Information-Based Objective Functions for Active Data Selection”, MacKay1992
  • “Bayes-Hermite Quadrature”, O’Hagan1991
  • “The 1988 Neyman Memorial Lecture: A Galtonian Perspective on Shrinkage Estimators”, Stigler1990
  • “Explanatory Coherence”, Thagard1989
  • “Informal Conceptions of Probability”
  • “The Double Exponential Distribution: Using Calculus to Find a Maximum Likelihood Estimator”, Norton1984
  • “This Week’s Citation Classic: Nearest Neighbor Pattern Classification”, Cover1982
  • “Lindley’s Paradox”, Shafer1982
  • “To Understand Regression from Parent to Offspring, Think Statistically”, Humphreys1978
  • “Stein‘s Paradox in Statistics: The Best Guess about the Future Is Usually Obtained by Computing the Average of past Events. Stein’s Paradox Defines Circumstances in Which There Are Estimators Better Than the Arithmetic Average”, Efron & Morris1977
  • “Interpreting Regression toward the Mean in Developmental Research”, Furby1973
  • “Computer-Aided Diagnosis Of Acute Abdominal Pain”, Dombal et al 1972
  • “Nearest Neighbor Pattern Classification”, Cover & Hart1967
  • “Inference in an Authorship Problem: A Comparative Study of Discrimination Methods Applied to the Authorship of the Disputed Federalist Papers”, Mosteller & Wallace1963
  • “Probability, Statistical Decision Theory, and Accounting”, Bierman1962
  • “A Statistical Paradox”, Lindley1957
  • “The Argentine Writer and Tradition”, Borges1951
  • “Probability and the Weighing of Evidence”, Good1950
  • “Evaluating the Effect of Inadequately Measured Variables in Partial Correlation Analysis”, Stouffer1936
  • “Interpretation of Educational Measurements”, Kelley1927
  • “Mr Keynes on Probability [Review of J. M. Keynes, A Treatise on Probability, 1921]”, Ramsey1922
  • “Philosophical Essay on Probabilities, Chapter 11: Concerning the Probabilities of Testimonies”, Laplace1814
  • “Shuffles, Bayes’ Theorem and Continuations.”
  • A Philosophical Essay on Probabilities, Laplace2024
  • “Why Generalization in RL Is Difficult: Epistemic POMDPs and Implicit Partial Observability [Blog]”
  • Bayesian Optimization Book
  • “An Experimental Design Perspective on Model-Based Reinforcement Learning [Blog]”
  • “In Praise of Sparsity and Convexity”, Tibshirani2024 (page 518)
  • “Brms: an R Package for Bayesian Generalized Multivariate Non-Linear Multilevel Models Using Stan”, Bürkner2024
  • “Approximate Bayesian Computation”, Sunnåker et al 2024
  • “Active Learning”
  • Probability Theory: The Logic Of Science, Jaynes2024
  • “Approximate Bayes Optimal Policy Search Using Neural Networks”
  • “Visualizing Bayes’ Theorem”
  • “Quantum-Bayesian and Pragmatist Views of Quantum Theory”
  • “Modelling a Time Series of Records With PyMC3”
  • “How a Kalman Filter Works, in Pictures”
  • “Research Update: Towards a Law of Iterated Expectations for Heuristic Estimators”
  • “Why We Can’t Take Expected Value Estimates Literally (Even When They’re Unbiased)”
  • “Why Neural Networks Generalise, and Why They Are (Kind Of) Bayesian”
  • “Language Models Model Us”
  • “Simple versus Short: Higher-Order Degeneracy and Error-Correction”
  • “A Time-Invariant Version of Laplace’s Rule”
  • “From Classical Methods to Generative Models: Tackling the Unreliability of Neuroscientific Measures in Mental Health Research”
  • “Tiny Data, Approximate Bayesian Computation and the Socks of Karl Broman”
  • “Probable Points and Credible Intervals, Part 2: Decision Theory”
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