Bayesian Approach for Adaptive Design

2010 ◽  
pp. 3-1-3-19 ◽  
Author(s):  
Guosheng Yin ◽  
Ying Yuan
Keyword(s):  
Bayesian Approach ◽  
Adaptive Design

What Works for Whom? A Bayesian Approach to Channeling Big Data Streams for Public Program Evaluation

2017 ◽  
Vol 39 (1) ◽  
pp. 109-122 ◽  
Author(s):  
Mariel McKenzie Finucane ◽  
Ignacio Martinez ◽  
Scott Cody
Keyword(s):  
Bayesian Approach ◽  
Data Streams ◽  
Adaptive Design ◽  
Public Programs ◽  
Bayesian Adaptive Design ◽  
Program Evaluations ◽  
What Works ◽  
Standard Design ◽  
Specific Subgroup ◽  
Big Data Streams

In the coming years, public programs will capture even more and richer data than they do now, including data from web-based tools used by participants in employment services, from tablet-based educational curricula, and from electronic health records for Medicaid beneficiaries. Program evaluators seeking to take full advantage of these data streams will require novel statistical methods, such as Bayesian approach. A Bayesian approach to randomized program evaluations efficiently identifies what works for whom. The Bayesian approach design adapts to accumulating evidence: Over the course of an evaluation, more study subjects are allocated to treatment arms that are more promising, given the specific subgroup from which each subject comes. We identify conditions under which there is more than a 90% chance that inference from the Bayesian adaptive design is superior to inference from a standard design, using less than one third the sample size.

Verbal statistical learning: Prior knowledge impacts word identification but not metacognition

2020 ◽  
Author(s):  
Laetitia Zmuda ◽  
Charlotte Baey ◽  
Paolo Mairano ◽  
Anahita Basirat
Keyword(s):  
Task Performance ◽  
Prior Knowledge ◽  
Statistical Learning ◽  
Cognitive Processes ◽  
Bayesian Approach ◽  
Word Identification ◽  
Artificial Language ◽  
Artificial Languages ◽  
Transitional Probabilities ◽  
Fully Bayesian

It is well-known that individuals can identify novel words in a stream of an artificial language using statistical dependencies. While underlying computations are thought to be similar from one stream to another (e.g. transitional probabilities between syllables), performance are not similar. According to the “linguistic entrenchment” hypothesis, this would be due to the fact that individuals have some prior knowledge regarding co-occurrences of elements in speech which intervene during verbal statistical learning. The focus of previous studies was on task performance. The goal of the current study is to examine the extent to which prior knowledge impacts metacognition (i.e. ability to evaluate one’s own cognitive processes). Participants were exposed to two different artificial languages. Using a fully Bayesian approach, we estimated an unbiased measure of metacognitive efficiency and compared the two languages in terms of task performance and metacognition. While task performance was higher in one of the languages, the metacognitive efficiency was similar in both languages. In addition, a model assuming no correlation between the two languages better accounted for our results compared to a model where correlations were introduced. We discuss the implications of our findings regarding the computations which underlie the interaction between input and prior knowledge during verbal statistical learning.

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