scholarly journals Predictive inference with the jackknife+

2021 ◽  
Vol 49 (1) ◽  
pp. 486-507
Author(s):  
Rina Foygel Barber ◽  
Emmanuel J. Candès ◽  
Aaditya Ramdas ◽  
Ryan J. Tibshirani
Keyword(s):  
Predictive Inference

Predictive Inference Activation

2012 ◽  
Vol 71 (3) ◽  
pp. 141-148 ◽  
Author(s):  
Doriane Gras ◽  
Hubert Tardieu ◽  
Serge Nicolas
Keyword(s):  
Naming Task ◽  
Word Naming ◽  
Predictive Inference ◽  
Text Reading ◽  
Predictive Inferences

Predictive inferences are anticipations of what could happen next in the text we are reading. These inferences seem to be activated during reading, but a delay is necessary for their construction. To determine the length of this delay, we first used a classical word-naming task. In the second experiment, we used a Stroop-like task to verify that inference activation was not due to strategies applied during the naming task. The results show that predictive inferences are naturally activated during text reading, after approximately 1 s.

scholarly journals Bootstrap predictive inference for ARIMA processes

2004 ◽  
Vol 25 (4) ◽  
pp. 449-465 ◽  
Author(s):  
Lorenzo Pascual ◽  
Juan Romo ◽  
Esther Ruiz
Keyword(s):  
Predictive Inference ◽  
Arima Processes

scholarly journals Predictive Inference Using Latent Variables with Covariates

Psychometrika ◽  
2014 ◽  
Vol 80 (3) ◽  
pp. 727-747 ◽  
Author(s):  
Lynne Steuerle Schofield ◽  
Brian Junker ◽  
Lowell J. Taylor ◽  
Dan A. Black
Keyword(s):  
Latent Variables ◽  
Predictive Inference

scholarly journals A Bayesian Account of Memory for Text

2019 ◽  
Author(s):  
Mark Andrews
Keyword(s):  
Bayesian Inference ◽  
Strong Evidence ◽  
Bayesian Model ◽  
Background Knowledge ◽  
Predictive Inference ◽  
Memory Representation ◽  
British English ◽  
Text Memory ◽  
Memory Experiment ◽  
Memory For Text

The study of memory for texts has had an long tradition of research in psychology. According to most general accounts, the recognition or recall of items in a text is based on querying a memory representation that is built up on the basis of background knowledge. The objective of this paper is to describe and thoroughly test a Bayesian model of these general accounts. In particular, we present a model that describes how we use our background knowledge to form memories in terms of Bayesian inference of statistical patterns in the text, followed by posterior predictive inference of the words that are typical of those inferred patterns. This provides us with precise predictions about which words will be remembered, whether veridically or erroneously, from any given text. We tested these predictions using behavioural data from a memory experiment using a large sample of randomly chosen texts from a representative corpus of British English. The results show that the probability of remembering any given word in the text, whether falsely or veridically, is well predicted by the Bayesian model. Moreover, compared to nontrivial alternative models of text memory, by every measure used in the analyses, the predictions of the Bayesian model were superior, often overwhelmingly so. We conclude that these results provide strong evidence in favour of the Bayesian account of text memory that we have presented in this paper.

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