Niels Van Hove’s Post

A Simple Bayesian Approach to Probability of Success I'm happy to share that my paper on "Estimating Predictive Probability of Success" was recently published by the International Institute of Forecasters in their Foresight Journal #72. In this paper, I show how Kahneman-Tversky’s (KT) original reference-class corrective procedure for intuitive forecasts can be reformulated using the language of Bayesian inference. I demonstrate two hypothetical examples for correcting overconfident probability of success estimates, using the Beta conjugate model. This approach may be useful for decision makers and forecasters wanting a quick, simple method to obtain probability of success estimates that are more consistent with actual historical outcomes. https://lnkd.in/dcCkZWWd

In today's fast-paced decision-making environments, achieving clarity and precision is paramount. Explore the intersection of cone distribution functions and multi-criteria decision making (MCDM) in our latest blog post. Discover an innovative procedure that surpasses traditional weighted sum scalarization by simultaneously accommodating multiple scalarizations. Understand the nuances of rank reversal and its analytical implications, along with practical insights through real-world examples and potential applications in machine learning. Propel your decision-making processes forward with this cutting-edge approach. Read the full blog post here: [Cone Ranking for Multi-Criteria Decision Making](https://bit.ly/4a6pLXL)

🎥  In this enlightening video, David Sontag simplifies the concept of causality, highlighting its application in the health area. The speaker addresses crucial causal questions and explores how these questions shape treatment decisions. It presents the Rubin-Neyman causal model, offering valuable insights for those seeking to understand cause-and-effect relationships in healthcare contexts. I highly recommend watching both videos to gain a comprehensive understanding of the topic: Part 1👉 https://lnkd.in/dpgew3MD Part 2👉 https://lnkd.in/dSi6iR9Z These videos are an integral part of a free online course offered by MIT OpenCourseWare 🚀, focused on introducing machine learning applied to healthcare. To explore more about the course and deepen your knowledge, visit: https://lnkd.in/deDpanQd   #research #data #datascientists #causality #causalinference #datascience #econometrics #machinelearning  #health

📢 Exciting News! Our latest blog post explores the fascinating concept of Subjective Causality. We delve into how decision makers' subjective causal judgements can be identified through their preferences over interventions. Drawing on established theories, we reveal how causal models can illuminate uncertainty and utility in decision-making. Dive into the details here: https://bit.ly/3S6kW8F [econ.TH]

🔗 Excited to share our latest research on process variant analysis across continuous features! Our novel framework offers a comprehensive perspective on process behavior, aiding organizations in improving efficiency and predicting outcomes. Check out the full article here: https://bit.ly/3XboZ7O. #ProcessAnalysis #DataScience #ResearchPublication

🔍 Explore a powerful decision-making framework that will transform the way you approach challenges and opportunities! Check out this insightful video: https://lnkd.in/gh6-PjZi" #DecisionMaking #ProblemSolving #ProfessionalDevelopment

It is crucial to prioritize strong emotional intelligence in today's fast-paced corporate landscape. Pairing this with artificial intelligence could effectively bridge any gaps that currently exist.

The Science Behind Probabilistic Reasoning: Making Sense of Complex Systems 🌟 Exciting News! 🌟 Check out our latest blog post that dives into "The Science Behind Probabilistic Reasoning: Making Sense of Complex Systems." 🧪🔍 In today's world, we often encounter complex systems and uncertain situations where probabilistic reasoning plays a crucial role. This powerful tool allows us to quantify uncertainty and make informed decisions based on available evidence. 📈 This article explores the concepts of probability, Bayesian inference, and probabilistic models, providing a comprehensive understanding of how to navigate through complexities. Discover how to update beliefs, analyze uncertainties, and make predictions in complex systems. 💡 Ready to explore the science behind probabilistic reasoning? 🤔 Check it out here: [link](https://ift.tt/SVn5tAW) 🚀 #dataanalytics #probabilisticreasoning #complexsystems #decisionmaking #analyticservices #blogpost https://ift.tt/SVn5tAW

Sign up now for our Introduction to Causal Inference course, where Mulusew J. Gerbaba Associate Research Scientist – Impact Evaluation at APHRC will dive into the methods of causal inference using observational data. You'll explore topics like potential outcomes, DAGs and causal graphs, target trial emulation, matching techniques, managing confounding and bias, causal structure learning, and quasi-experimental methods. Don't miss this opportunity to acquaint yourself with modern causal modeling methodology. Register today! https://lnkd.in/dcMnHVzX #MentalHealthDataPrizeAfrica #MentalHealthAwareness #DataForChange

【Game Theory Based Model for Predictive Analytics Using Distributed Position Function】 Full article: https://lnkd.in/gN3tFzAN (Authored by Mirhossein Mousavi Karimi and Shahram Rahimi, from Mississippi State University, USA.)   The ability to predict the outcome of a negotiation is an important topic in various fields. This paper introduces a #game_theory-based approach for forecasting outcomes of negotiation and group decision-making problems and proposes an extension to the BDM model that addresses situations where actors' positions span a spectrum, thus offering increased flexibility in defining their targets. #Group_Decision_Making #Predictive_Analytics