Norman Fenton
Biographic Data
| ID | 3633625 |
|---|---|
| NAME | Norman Fenton |
| GIVEN NAMES | Norman |
| FAMILY NAME | Fenton |
| SIGNATURE | FENTON N |
| AFFILIATIONS | Queen Mary University of London |
| ORCID | 0000-0003-2924-0510 |
| VERIFIED | Yes |
| TOTAL WORKS | 13 |
| TOTAL CITATIONS | 6 |
| AUTHOR COUNT | 11 |
| EDITOR COUNT | 2 |
| FIRST PUBLICATION YEAR | 1925 |
| LATEST PUBLICATION YEAR | 2023 |
| H-INDEX | 1 |
The Correctional Community: An Introduction and Guide
Dependencies in evidential reports: The case for informational advantages
Covid-19 infection and death rates: The need to incorporate causal explanations for the data and avoid bias in testing
COVID-19 testing strategies are primarily driven by medical need - focusing on people already hospitalized with significant symptoms or on people most at risk. However, such testing is highly biased because it fails to identify the extent to which COVID-19 is present in people with mild or no symptoms. If we wish to understand the true rate of COVID-19 infection and death, we need to take full account of the causal explanations for the resulting …
Bayesian network analysis of Covid-19 data reveals higher infection prevalence rates and lower fatality rates than widely reported
Widely reported statistics on Covid-19 across the globe fail to take account of both the uncertainty of the data and possible explanations for this uncertainty. In this article, we use a Bayesian Network (BN) model to estimate the Covid-19 infection prevalence rate (IPR) and infection fatality rate (IFR) for different countries and regions, where relevant data are available. This combines multiple sources of data in a single model. The results sh…
Causality, the critical but often ignored component guiding us through a world of uncertainties in risk assessment
The idea of uncertainty analyses, which typically involves quantification, is to protect practitioners and consumers from drawing unsubstantiated conclusions from scientific assessments of risk. The importance of causal modelling in this process – along with the inference methods associated with such modelling – is now increasingly widely recognized; yet organizations responsible for policy on uncertainty and risk in critical domains have general…
The Correctional Community
The Prisoner's Family: A Study of Family Counseling in an Adult Correctional System
The Process of Reception in the Adult Correctional System
The Open Mind: Elmer Ernest Southard, 1876-1920
The Clinical Treatment of the Problem Child
The Delinquent Boy and the Correctional School
The Delinquent Boy and the Correctional School
Anticipation neurosis and army morale
In the anticipation neurosis, the novelty of response comes from the fact that the stimuli are all developed by the patient and spring from his own imagination. The importance of the anticipatory type of neurosis was not in their frequency nor in their clinical appearance; but in the fact that they show that fear is so primal a quality that the organism responds relatively quickly to the expectancy of danger as well as to the fact. Further, the e…
Causality, the critical but often ignored component guiding us through a world of uncertainties in risk assessment
The idea of uncertainty analyses, which typically involves quantification, is to protect practitioners and consumers from drawing unsubstantiated conclusions from scientific assessments of risk. The importance of causal modelling in this process – along with the inference methods associated with such modelling – is now increasingly widely recognized; yet organizations responsible for policy on uncertainty and risk in critical domains have general…
Covid-19 infection and death rates: The need to incorporate causal explanations for the data and avoid bias in testing
COVID-19 testing strategies are primarily driven by medical need - focusing on people already hospitalized with significant symptoms or on people most at risk. However, such testing is highly biased because it fails to identify the extent to which COVID-19 is present in people with mild or no symptoms. If we wish to understand the true rate of COVID-19 infection and death, we need to take full account of the causal explanations for the resulting …
Anticipation neurosis and army morale
In the anticipation neurosis, the novelty of response comes from the fact that the stimuli are all developed by the patient and spring from his own imagination. The importance of the anticipatory type of neurosis was not in their frequency nor in their clinical appearance; but in the fact that they show that fear is so primal a quality that the organism responds relatively quickly to the expectancy of danger as well as to the fact. Further, the e…
The Delinquent Boy and the Correctional School
The Delinquent Boy and the Correctional School
The Open Mind: Elmer Ernest Southard, 1876-1920
The Clinical Treatment of the Problem Child
The Process of Reception in the Adult Correctional System
The Prisoner's Family: A Study of Family Counseling in an Adult Correctional System
The Correctional Community
Causality, the critical but often ignored component guiding us through a world of uncertainties in risk assessment
The idea of uncertainty analyses, which typically involves quantification, is to protect practitioners and consumers from drawing unsubstantiated conclusions from scientific assessments of risk. The importance of causal modelling in this process – along with the inference methods associated with such modelling – is now increasingly widely recognized; yet organizations responsible for policy on uncertainty and risk in critical domains have general…
Dependencies in evidential reports: The case for informational advantages
Covid-19 infection and death rates: The need to incorporate causal explanations for the data and avoid bias in testing
COVID-19 testing strategies are primarily driven by medical need - focusing on people already hospitalized with significant symptoms or on people most at risk. However, such testing is highly biased because it fails to identify the extent to which COVID-19 is present in people with mild or no symptoms. If we wish to understand the true rate of COVID-19 infection and death, we need to take full account of the causal explanations for the resulting …
Bayesian network analysis of Covid-19 data reveals higher infection prevalence rates and lower fatality rates than widely reported
Widely reported statistics on Covid-19 across the globe fail to take account of both the uncertainty of the data and possible explanations for this uncertainty. In this article, we use a Bayesian Network (BN) model to estimate the Covid-19 infection prevalence rate (IPR) and infection fatality rate (IFR) for different countries and regions, where relevant data are available. This combines multiple sources of data in a single model. The results sh…
The Correctional Community: An Introduction and Guide
Psychology (8 works) · Computer Science (5 works) · Criminal Justice and Corrections Analysis (5 works) · Artificial Intelligence (3 works) · 2019-20 coronavirus outbreak (2 works) · Bayesian probability (2 works) · Causality (physics (2 works) · Cognitive psychology (2 works) · Coronavirus disease 2019 (COVID-19 (2 works) · COVID-19 epidemiological studies (2 works)