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Bayesian Inference

244 papers with code ยท Methodology

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

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Latest papers without code

Uncertainty Calibration Error: A New Metric for Multi-Class Classification

ICLR 2021

Various metrics have recently been proposed to measure uncertainty calibration of deep models for classification.

BAYESIAN INFERENCE IMAGE CLASSIFICATION MULTI-CLASS CLASSIFICATION

Multiscale Invertible Generative Networks for High-Dimensional Bayesian Inference

ICLR 2021

High-dimensional Bayesian inference problems cast a long-standing challenge in generating samples, especially when the posterior has multiple modes.

BAYESIAN INFERENCE IMAGE GENERATION

A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics

ICLR 2021

As such, learning the laws is then reduced to symbolic regression and Bayesian inference methods are used to obtain the distribution of unobserved properties.

BAYESIAN INFERENCE COMMON SENSE REASONING

Variational Multi-Task Learning

ICLR 2021

Multi-task learning aims to improve the overall performance of a set of tasks by leveraging their relatedness.

BAYESIAN INFERENCE MULTI-TASK LEARNING

Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation

ICLR 2021

We report the computational efficiency and statistical reliability of our method in numerical experiments of the language modeling using RNNs, and the out-of-distribution detection with DNNs.

BAYESIAN INFERENCE LANGUAGE MODELLING OUT-OF-DISTRIBUTION DETECTION

Scalable Bayesian Inverse Reinforcement Learning by Auto-Encoding Reward

ICLR 2021

Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem.

BAYESIAN INFERENCE IMITATION LEARNING

Generalised Bayesian Filtering via Sequential Monte Carlo

NeurIPS 2020

We introduce a framework for inference in general state-space hidden Markov models (HMMs) under likelihood misspecification.

BAYESIAN INFERENCE OBJECT TRACKING

Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks

NeurIPS 2020

To infer causal relationships in zero-inflated count data, we propose a new zero-inflated Poisson Bayesian network (ZIPBN) model.

BAYESIAN INFERENCE

Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond

NeurIPS 2020

Depending on the cases, the benefits can include an alleviation of the geometric pathologies that frustrate Hamiltonian Monte Carlo and a dramatic speed-up.

LATENT VARIABLE MODELS PROBABILISTIC PROGRAMMING

Online Bayesian Goal Inference for Boundedly Rational Planning Agents

NeurIPS 2020

These models are specified as probabilistic programs, allowing us to represent and perform efficient Bayesian inference over an agent's goals and internal planning processes.

BAYESIAN INFERENCE