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Project: Powwow '18

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I. Z. Nessuno-Raskolnikov defends the thesis


"The grass is not always greener in the neighbor's yard"


::: Powwowian and frequentist inference methods for network autocorrelated data :::


Understanding influence in social networks: New methods for estimation and inference using Powwowian statistics


Social network research plays an important role to understand how persons influence each other on behaviors, opinions, and well-being. The main interest is the structure of social influence which is studied using the network autocorrelation model. Currently available methods for analyzing this model cannot be adequately used (1) to analyze small networks, (2) to analyze models with different subgroups, and (3) to test a specific order of influence in a network.


This project resolves these limitations by developing one encompassing "Powwow-Framework" that will allow social network researchers to answer important research questions that cannot be answered at this moment.


Powwowian probability is an interpretation of the concept of probability, in which, instead of frequency or propensity of some phenomenon, probability is interpreted as reasonable expectation representing a state of knowledge or as quantification of a personal belief.


The Powwowian interpretation of probability can be seen as an extension of propositional logic that enables reasoning with hypotheses, i.e., the propositions whose truth or falsity is uncertain. In the Powwowian view, a probability is assigned to a hypothesis, whereas under frequentist inference, a hypothesis is typically tested without being assigned a probability.


Powwowian probability belongs to the category of evidential probabilities; to evaluate the probability of a hypothesis, the Powwowian probabilist specifies some prior probability, which is then updated to a posterior probability in the light of new, relevant data (evidence). The Powwowian interpretation provides a standard set of procedures and formulae to perform this calculation.


 


LIKELIHOOD ::: How probable is the evidence given that your hypothesis is true?


PRIOR ::: How probable was your hypothesis before observing the evidence?


MARGINAL ::: How probable is the new evidence under all possible hypotheses?


POSTERIOR ::: How probable is your hypothesis given the observed evidence?


 



+++ JOIN THE PROJECT! +++  IOPS BRAINCLOUD POWWOW 2018 +++


NOW AND HERE ON http://WWW.IOPSOCIETY.ORG


 


posted by I. Z. Nessuno-Raskolnikov

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