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Big Data--broadly considered as datasets whose size, complexity, and heterogeneity preclude conventional approaches to storage and analysis--continues to generate interest across many scientific domains in both the public and private sectors. However, analyses of large heterogeneous datasets can suffer from unidentified bias, misleading correlations, and increased risk of false positives. In order for the proliferation of data to produce new scientific discoveries, it is essential that the statistical models used for analysis support reliable, reproducible inference. The National Academies of Sciences, Engineering, and Medicine convened a workshop to discuss how scientific inference should be applied when working with large, complex datasets.

Inference. --- Big data.

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"Everyone has heard the claim, "Correlation does not imply causation." What might sound like a reasonable dictum metastasized in the twentieth century into one of science's biggest obstacles, as a legion of researchers became unwilling to make the claim that one thing could cause another. Even two decades ago, asking a statistician a question like "Was it the aspirin that stopped my headache?" would have been like asking if he believed in voodoo, or at best a topic for conversation at a cocktail party rather than a legitimate target of scientific inquiry. Scientists were allowed to posit only that the probability that one thing was associated with another. This all changed with Judea Pearl, whose work on causality was not just a victory for common sense, but a revolution in the study of the world"--

Causation. --- Inference. --- Philosophy of nature --- Causation --- Inference --- Causalité --- Inférence. --- Causalité --- Inférence.

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Certain combinations of sounds or signs on paper are meaningful. What makes it the case that, unlike most combinations of sounds answers to these questions are based on the idea that words stand for something, but it is difficult to say what words such as good, if, or probable stand for. This book advances novel answers based on the idea that words get their meaning from the way they are used to express states of mind and what follows from them. It articulates a precise version of this idea, at a time when the shortcomings of the traditional answers are hotly discussed.

Expression. --- Semantics. --- Inference. --- Language. --- Language: reference & general.

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This book is about abduction, 'the logic of Sherlock Holmes', and about how some kinds of abductive reasoning can be programmed in a computer. The work brings together Artificial Intelligence and philosophy of science and is rich with implications for other areas such as, psychology, medical informatics, and linguistics. It also has subtle implications for evidence evaluation in areas such as accident investigation, confirmation of scientific theories, law, diagnosis, and financial auditing. The book is about certainty and the logico-computational foundations of knowledge; it is about inference in perception, reasoning strategies, and building expert systems.

Abduction (Logic) --- Inference. --- Knowledge, Theory of.

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This work provides a framework for info-metrics-the science of modeling, inference, and reasoning under conditions of noisy and insufficient information. Info-metrics is an inherently interdisciplinary framework that emerged from the intersection of information theory, statistical inference, and decision-making under uncertainty. It allows us to process the available information with minimal reliance on assumptions that cannot be validated. This text focuses on unifying all information processing and model building within a single constrained optimization framework. It provides a complete framework for modeling and inference, rather than a problem-specific model.

Measurement uncertainty (Statistics) --- Inference. --- Mathematical statistics.