Efficient computation of predictive probabilities in probit models via expectation propagation

• URL: http://arxiv.org/abs/2309.01630v1
• Date: Mon, 4 Sep 2023 14:21:44 GMT
• Title: Efficient computation of predictive probabilities in probit models via expectation propagation
• Authors: Augusto Fasano, Niccol\`o Anceschi, Beatrice Franzolini, Giovanni Rebaudo
• Abstract summary: We focus on the computation of predictive probabilities in Bayesian probit models via expectation propagation (EP) We show that such predictive probabilities admit a closed-form expression. Improvements over state-of-the-art approaches are shown in a simulation study.
• Score: 1.433758865948252
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Binary regression models represent a popular model-based approach for binary classification. In the Bayesian framework, computational challenges in the form of the posterior distribution motivate still-ongoing fruitful research. Here, we focus on the computation of predictive probabilities in Bayesian probit models via expectation propagation (EP). Leveraging more general results in recent literature, we show that such predictive probabilities admit a closed-form expression. Improvements over state-of-the-art approaches are shown in a simulation study.

Related papers

• Self-Boost via Optimal Retraining: An Analysis via Approximate Message Passing [58.52119063742121]
  Retraining a model using its own predictions together with the original, potentially noisy labels is a well-known strategy for improving the model performance.<n>This paper addresses the question of how to optimally combine the model's predictions and the provided labels.<n>Our main contribution is the derivation of the Bayes optimal aggregator function to combine the current model's predictions and the given labels.
  arXiv  Detail & Related papers  (2025-05-21T07:16:44Z)
• Generative Modeling with Bayesian Sample Inference [50.07758840675341]
  We derive a novel generative model from the simple act of Gaussian posterior inference. Treating the generated sample as an unknown variable to infer lets us formulate the sampling process in the language of Bayesian probability. Our model uses a sequence of prediction and posterior update steps to narrow down the unknown sample from a broad initial belief.
  arXiv  Detail & Related papers  (2025-02-11T14:27:10Z)
• Supervised Score-Based Modeling by Gradient Boosting [49.556736252628745]
  We propose a Supervised Score-based Model (SSM) which can be viewed as a gradient boosting algorithm combining score matching. We provide a theoretical analysis of learning and sampling for SSM to balance inference time and prediction accuracy. Our model outperforms existing models in both accuracy and inference time.
  arXiv  Detail & Related papers  (2024-11-02T07:06:53Z)
• Estimating Causal Effects from Learned Causal Networks [56.14597641617531]
  We propose an alternative paradigm for answering causal-effect queries over discrete observable variables. We learn the causal Bayesian network and its confounding latent variables directly from the observational data. We show that this emphmodel completion learning approach can be more effective than estimand approaches.
  arXiv  Detail & Related papers  (2024-08-26T08:39:09Z)
• A variational neural Bayes framework for inference on intractable posterior distributions [1.0801976288811024]
  Posterior distributions of model parameters are efficiently obtained by feeding observed data into a trained neural network. We show theoretically that our posteriors converge to the true posteriors in Kullback-Leibler divergence.
  arXiv  Detail & Related papers  (2024-04-16T20:40:15Z)
• Efficient Incremental Belief Updates Using Weighted Virtual Observations [2.7195102129095003]
  We present an algorithmic solution to the problem of incremental belief updating in the context of Monte Carlo inference. We implement and apply the solution to a number of didactic examples and case studies, showing efficiency and robustness of our approach.
  arXiv  Detail & Related papers  (2024-02-10T12:48:49Z)
• Structured Radial Basis Function Network: Modelling Diversity for Multiple Hypotheses Prediction [51.82628081279621]
  Multi-modal regression is important in forecasting nonstationary processes or with a complex mixture of distributions. A Structured Radial Basis Function Network is presented as an ensemble of multiple hypotheses predictors for regression problems. It is proved that this structured model can efficiently interpolate this tessellation and approximate the multiple hypotheses target distribution.
  arXiv  Detail & Related papers  (2023-09-02T01:27:53Z)
• Model Predictive Control with Gaussian-Process-Supported Dynamical Constraints for Autonomous Vehicles [82.65261980827594]
  We propose a model predictive control approach for autonomous vehicles that exploits learned Gaussian processes for predicting human driving behavior. A multi-mode predictive control approach considers the possible intentions of the human drivers.
  arXiv  Detail & Related papers  (2023-03-08T17:14:57Z)
• Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference [0.0]
  This paper presents a fast algorithm for estimating hidden states of Bayesian state space models. After pretraining, finding the posterior distribution for any dataset takes from hundredths to tenths of a second.
  arXiv  Detail & Related papers  (2022-10-13T16:37:05Z)
• Bayesian Neural Network Inference via Implicit Models and the Posterior Predictive Distribution [0.8122270502556371]
  We propose a novel approach to perform approximate Bayesian inference in complex models such as Bayesian neural networks. The approach is more scalable to large data than Markov Chain Monte Carlo. We see this being useful in applications such as surrogate and physics-based models.
  arXiv  Detail & Related papers  (2022-09-06T02:43:19Z)
• Pathologies of Pre-trained Language Models in Few-shot Fine-tuning [50.3686606679048]
  We show that pre-trained language models with few examples show strong prediction bias across labels. Although few-shot fine-tuning can mitigate the prediction bias, our analysis shows models gain performance improvement by capturing non-task-related features. These observations alert that pursuing model performance with fewer examples may incur pathological prediction behavior.
  arXiv  Detail & Related papers  (2022-04-17T15:55:18Z)
• Distributional Gradient Boosting Machines [77.34726150561087]
  Our framework is based on XGBoost and LightGBM. We show that our framework achieves state-of-the-art forecast accuracy.
  arXiv  Detail & Related papers  (2022-04-02T06:32:19Z)
• Latent Gaussian Model Boosting [0.0]
  Tree-boosting shows excellent predictive accuracy on many data sets. We obtain increased predictive accuracy compared to existing approaches in both simulated and real-world data experiments.
  arXiv  Detail & Related papers  (2021-05-19T07:36:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.