BAMITA: Bayesian Multiple Imputation for Tensor Arrays

• URL: http://arxiv.org/abs/2410.23412v1
• Date: Wed, 30 Oct 2024 19:30:23 GMT
• Title: BAMITA: Bayesian Multiple Imputation for Tensor Arrays
• Authors: Ziren Jiang, Gen Li, Eric F. Lock,
• Abstract summary: We propose a multiple imputation approach for tensors in a flexible Bayesian framework. Our model uses efficient and widely applicable conjugate priors for a CANDECOMP/PARAFAC (CP) factorization. It is shown to accurately capture uncertainty in the full microbiome profile at missing timepoints and used to infer trends in species diversity for the population.
• Score: 4.111762232988317
• License:
• Abstract: Data increasingly take the form of a multi-way array, or tensor, in several biomedical domains. Such tensors are often incompletely observed. For example, we are motivated by longitudinal microbiome studies in which several timepoints are missing for several subjects. There is a growing literature on missing data imputation for tensors. However, existing methods give a point estimate for missing values without capturing uncertainty. We propose a multiple imputation approach for tensors in a flexible Bayesian framework, that yields realistic simulated values for missing entries and can propagate uncertainty through subsequent analyses. Our model uses efficient and widely applicable conjugate priors for a CANDECOMP/PARAFAC (CP) factorization, with a separable residual covariance structure. This approach is shown to perform well with respect to both imputation accuracy and uncertainty calibration, for scenarios in which either single entries or entire fibers of the tensor are missing. For two microbiome applications, it is shown to accurately capture uncertainty in the full microbiome profile at missing timepoints and used to infer trends in species diversity for the population. Documented R code to perform our multiple imputation approach is available at https://github.com/lockEF/MultiwayImputation .

Related papers

• Bayes-CATSI: A variational Bayesian deep learning framework for medical time series data imputation [0.196629787330046]
  We propose a Bayesian Context-Aware Time Series Imputation (Bayes-CATSI) framework. We consider the time series derived from electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), electrocardiology (EKG) Our results show that Bayes-CATSI not only provides uncertainty quantification but also achieves superior imputation performance compared to the CATSI model.
  arXiv  Detail & Related papers  (2024-10-01T07:37:52Z)
• MTSCI: A Conditional Diffusion Model for Multivariate Time Series Consistent Imputation [41.681869408967586]
  Key research question is how to ensure imputation consistency, i.e., intra-consistency between observed and imputed values. Previous methods rely solely on the inductive bias of the imputation targets to guide the learning process.
  arXiv  Detail & Related papers  (2024-08-11T10:24:53Z)
• Probabilistic Imputation for Time-series Classification with Missing Data [17.956329906475084]
  We propose a novel framework for classification with time series data with missing values. Our deep generative model part is trained to impute the missing values in multiple plausible ways. The classifier part takes the time series data along with the imputed missing values and classifies signals.
  arXiv  Detail & Related papers  (2023-08-13T10:04:13Z)
• Bayesian predictive modeling of multi-source multi-way data [0.0]
  We consider molecular data from multiple 'omics sources as predictors of early-life iron deficiency (ID) in a rhesus monkey model. We use a linear model with a low-rank structure on the coefficients to capture multi-way dependence. We show that our model performs as expected in terms of misclassification rates and correlation of estimated coefficients with true coefficients.
  arXiv  Detail & Related papers  (2022-08-05T21:58:23Z)
• Uncertainty Modeling for Out-of-Distribution Generalization [56.957731893992495]
  We argue that the feature statistics can be properly manipulated to improve the generalization ability of deep learning models. Common methods often consider the feature statistics as deterministic values measured from the learned features. We improve the network generalization ability by modeling the uncertainty of domain shifts with synthesized feature statistics during training.
  arXiv  Detail & Related papers  (2022-02-08T16:09:12Z)
• TACTiS: Transformer-Attentional Copulas for Time Series [76.71406465526454]
  estimation of time-varying quantities is a fundamental component of decision making in fields such as healthcare and finance. We propose a versatile method that estimates joint distributions using an attention-based decoder. We show that our model produces state-of-the-art predictions on several real-world datasets.
  arXiv  Detail & Related papers  (2022-02-07T21:37:29Z)
• MURAL: An Unsupervised Random Forest-Based Embedding for Electronic Health Record Data [59.26381272149325]
  We present an unsupervised random forest for representing data with disparate variable types. MURAL forests consist of a set of decision trees where node-splitting variables are chosen at random. We show that using our approach, we can visualize and classify data more accurately than competing approaches.
  arXiv  Detail & Related papers  (2021-11-19T22:02:21Z)
• Imputation-Free Learning from Incomplete Observations [73.15386629370111]
  We introduce the importance of guided gradient descent (IGSGD) method to train inference from inputs containing missing values without imputation. We employ reinforcement learning (RL) to adjust the gradients used to train the models via back-propagation. Our imputation-free predictions outperform the traditional two-step imputation-based predictions using state-of-the-art imputation methods.
  arXiv  Detail & Related papers  (2021-07-05T12:44:39Z)
• Contrastive learning of strong-mixing continuous-time stochastic processes [53.82893653745542]
  Contrastive learning is a family of self-supervised methods where a model is trained to solve a classification task constructed from unlabeled data. We show that a properly constructed contrastive learning task can be used to estimate the transition kernel for small-to-mid-range intervals in the diffusion case.
  arXiv  Detail & Related papers  (2021-03-03T23:06:47Z)
• Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula [30.84155327760468]
  This paper proposes a framework for missing value imputation with quantified uncertainty. The time required to fit the model scales linearly with the number of rows and the number of columns in the dataset. Empirical results show the method yields state-of-the-art imputation accuracy across a wide range of data types.
  arXiv  Detail & Related papers  (2020-06-18T19:51:42Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)

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.