Variational Prediction
Bayesian inference offers benefits over maximum likelihood, but it also comes with computational costs. Computing the posterior is typically intractable, as is marginalizing that posterior to form the posterior predictive distribution. In this paper, we present variational prediction, a technique for directly learning a variational approximation to the posterior predictive distribution using a variational bound. This approach can provide good predictive distributions without test time marginalization costs. We demonstrate Variational Prediction on an illustrative toy example.
Code (0)
등록된 구현이 없습니다.
Tasks
Bayesian InferencePredictionSimilar Papers 제목 키워드 기반
A Log-likelihood Regularized KL Divergence for Video Prediction with A 3D Convolutional Variational Recurrent Network
The use of latent variable models has shown to be a powerful tool for modeling probability distributions over sequences. In this paper, we introduce a new variational model that extends the recurrent network in two ways …
PredictionVideo PredictionExplain Variance of Prediction in Variational Time Series Models for Clinical Deterioration Prediction
Missingness and measurement frequency are two sides of the same coin. How frequent should we measure clinical variables and conduct laboratory tests? It depends on many factors such as the stability of patient conditions…
AttributeDecision MakingDiagnosticImputation+2Towards Generalizable and Interpretable Motion Prediction: A Deep Variational Bayes Approach
Estimating the potential behavior of the surrounding human-driven vehicles is crucial for the safety of autonomous vehicles in a mixed traffic flow. Recent state-of-the-art achieved accurate prediction using deep neural …
Autonomous Vehiclesmotion predictionPredictionVAE-Var: Variational-Autoencoder-Enhanced Variational Assimilation
Data assimilation refers to a set of algorithms designed to compute the optimal estimate of a system's state by refining the prior prediction (known as background states) using observed data. Variational assimilation met…
Traffic Flow Prediction via Variational Bayesian Inference-based Encoder-Decoder Framework
Accurate traffic flow prediction, a hotspot for intelligent transportation research, is the prerequisite for mastering traffic and making travel plans. The speed of traffic flow can be affected by roads condition, weathe…
Bayesian InferenceDecoderPredictionVariational Inference