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Statistical Inference
In the previous chapter we have seen how to describe a sample in order to produce potentially interesting hypotheses about its population. Some of...
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Multi-sensory system for UAVs detection using Bayesian inference
Unmanned Aerial Vehicles UAVs have revolutionized a wide range of activities and businesses, creating new opportunities for commercial and military...
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Inference of phylogenetic trees directly from raw sequencing reads using Read2Tree
Current methods for inference of phylogenetic trees require running complex pipelines at substantial computational and labor costs, with additional...
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Statistical inference and effect measures in abstracts of randomized controlled trials, 1975–2021. A systematic review
ObjectiveTo examine the time trend of statistical inference, statistical reporting style of results, and effect measures from the abstracts of...
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A grammatical evolution approach to the automatic inference of P systems
P systems are a bio-inspired framework for defining parallel models of computation. Despite their relevance for both theoretical and application...
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Continual Inference: A Library for Efficient Online Inference with Deep Neural Networks in PyTorch
We present Continual Inference, a Python library for implementing Continual Inference Networks (CINs), a class of Neural Networks designed for...
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Direct inference of Patlak parametric images in whole-body PET/CT imaging using convolutional neural networks
PurposeThis study proposed and investigated the feasibility of estimating Patlak-derived influx rate constant ( K i ) from standardized uptake value...
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A note on sufficient dimension reduction with post dimension reduction statistical inference
Sufficient dimension reduction is a widely used tool to extract core information hidden in high-dimensional data for classifying, clustering, and...
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Reactive and Proactive Methods for Database Protection against Logical Inference Attacks
AbstractIf data is not available to the outside world, it is useless. It must be available so that it can be processed and planned. Regulating and...
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Bias-reduced and variance-corrected asymptotic Gaussian inference about extreme expectiles
The expectile is a prime candidate for being a standard risk measure in actuarial and financial contexts, for its ability to recover information...
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Interpreting frequentist hypothesis tests: insights from Bayesian inference
Randomized controlled trials are one of the best ways of quantifying the effectiveness of medical interventions. Therefore, when the authors of a...
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Causal Inference on Graphs
Graph is a powerful tool for modeling complex relational information between data points. Studies on graphs have been widely applied in many...
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EnsInfer: a simple ensemble approach to network inference outperforms any single method
This study evaluates both a variety of existing base causal inference methods and a variety of ensemble methods. We show that: (i) base network...
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Understanding capacity fade in organic redox-flow batteries by combining spectroscopy with statistical inference techniques
Organic redox-active molecules are attractive as redox-flow battery (RFB) reactants because of their low anticipated costs and widely tunable...
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Causal inference from cross-sectional earth system data with geographical convergent cross map**
Causal inference in complex systems has been largely promoted by the proposal of some advanced temporal causation models. However, temporal models...
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Map** Husserlian Phenomenology onto Active Inference
Phenomenology is the rigorous descriptive study of conscious experience. Recent attempts to formalize Husserlian phenomenology provide us with a...
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A Bayesian generative neural network framework for epidemic inference problems
The reconstruction of missing information in epidemic spreading on contact networks can be essential in the prevention and containment strategies....
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Gene regulatory network inference in the era of single-cell multi-omics
The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory...
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MR-GGI: accurate inference of gene–gene interactions using Mendelian randomization
BackgroundResearchers have long studied the regulatory processes of genes to uncover their functions. Gene regulatory network analysis is one of the...
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A robust and accurate single-cell data trajectory inference method using ensemble pseudotime
BackgroundThe advance in single-cell RNA sequencing technology has enhanced the analysis of cell development by profiling heterogeneous cells in...
