Giuseppe Arena
Biographic Data
| ID | 4163902 |
|---|---|
| NAME | Giuseppe Arena |
| GIVEN NAMES | Giuseppe |
| FAMILY NAME | Arena |
| SIGNATURE | ARENA G |
| AFFILIATIONS | Tilburg University |
| ORCID | 0000-0001-5204-3326 |
| VERIFIED | Yes |
| TOTAL WORKS | 5 |
| TOTAL CITATIONS | 7 |
| WORKS AS AUTHOR | 5 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 2 |
LLM‐based prior elicitation for Bayesian graphical modeling
In the Bayesian graphical modeling framework, priors on network structure encode theoretical assumptions and uncertainty about the topology of psychological constructs under study. For instance, the Bernoulli prior specifies the probability of each pairwise interaction, the Beta–Bernoulli prior governs expected network density, and the Stochastic Block prior models clustering. In practice, however, specifying informed hyperparameters is challengi…
A Review of Manuale di letteratura elettronica
This review analyzes Fabrizio Venerandi’s Manuale di letteratura elettronica (2024), a work that aims to introduce a non-specialist audience to the world of born-digital literature, with a particular focus on text adventures and narrative video games. The volume highlights the centrality of video games as "new literature" and their ability to redefine the traditional categories of author, reader and text, combining a historical perspective with a…
Avant-Texts, Characters and Factoids
This study introduces the Real-To-Fictional Ontology (RTFO), a structured framework designed to analyze the dynamic relationship between reality and fiction in literary works, with a focus on preparatory materials and their influence on narrative construction. While traditional Italian philology and genetic criticism have distinct theoretical and editorial approaches to avant-text, this ontology addresses their limitations by integrating fine-gra…
A Bayesian Semi-Parametric Approach for Modeling Memory Decay in Dynamic Social Networks
In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactions and the time that has elapsed since the past interactions affect the actors' decision-making to interact with other actors in the network. Recently occurred events may have a stronger influence on current interaction behavior than past events that oc…
How fast do we forget our past social interactions? Understanding memory retention with parametric decays in relational event models
In relational event networks, endogenous statistics are used to summarize the past activity between actors. Typically, it is assumed that past events have equal weight on the social interaction rate in the (near) future regardless of the time that has transpired since observing them. Generally, it is unrealistic to assume that recently past events affect the current event rate to an equal degree as long-past events. Alternatively one may consider…
How fast do we forget our past social interactions? Understanding memory retention with parametric decays in relational event models
In relational event networks, endogenous statistics are used to summarize the past activity between actors. Typically, it is assumed that past events have equal weight on the social interaction rate in the (near) future regardless of the time that has transpired since observing them. Generally, it is unrealistic to assume that recently past events affect the current event rate to an equal degree as long-past events. Alternatively one may consider…
A Bayesian Semi-Parametric Approach for Modeling Memory Decay in Dynamic Social Networks
In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactions and the time that has elapsed since the past interactions affect the actors' decision-making to interact with other actors in the network. Recently occurred events may have a stronger influence on current interaction behavior than past events that oc…
How fast do we forget our past social interactions? Understanding memory retention with parametric decays in relational event models
In relational event networks, endogenous statistics are used to summarize the past activity between actors. Typically, it is assumed that past events have equal weight on the social interaction rate in the (near) future regardless of the time that has transpired since observing them. Generally, it is unrealistic to assume that recently past events affect the current event rate to an equal degree as long-past events. Alternatively one may consider…
A Bayesian Semi-Parametric Approach for Modeling Memory Decay in Dynamic Social Networks
In relational event networks, the tendency for actors to interact with each other depends greatly on the past interactions between the actors in a social network. Both the volume of past interactions and the time that has elapsed since the past interactions affect the actors' decision-making to interact with other actors in the network. Recently occurred events may have a stronger influence on current interaction behavior than past events that oc…
Avant-Texts, Characters and Factoids
This study introduces the Real-To-Fictional Ontology (RTFO), a structured framework designed to analyze the dynamic relationship between reality and fiction in literary works, with a focus on preparatory materials and their influence on narrative construction. While traditional Italian philology and genetic criticism have distinct theoretical and editorial approaches to avant-text, this ontology addresses their limitations by integrating fine-gra…
LLM‐based prior elicitation for Bayesian graphical modeling
In the Bayesian graphical modeling framework, priors on network structure encode theoretical assumptions and uncertainty about the topology of psychological constructs under study. For instance, the Bernoulli prior specifies the probability of each pairwise interaction, the Beta–Bernoulli prior governs expected network density, and the Stochastic Block prior models clustering. In practice, however, specifying informed hyperparameters is challengi…
A Review of Manuale di letteratura elettronica
This review analyzes Fabrizio Venerandi’s Manuale di letteratura elettronica (2024), a work that aims to introduce a non-specialist audience to the world of born-digital literature, with a particular focus on text adventures and narrative video games. The volume highlights the centrality of video games as "new literature" and their ability to redefine the traditional categories of author, reader and text, combining a historical perspective with a…
Bayesian probability (3 works) · Bayesian Modeling and Causal Inference (2 works) · Complex Network Analysis Techniques (2 works) · Computer Science (2 works) · Digital Humanities and Scholarship (2 works) · Mathematics (2 works) · Mental Health Research Topics (2 works) · Parametric statistics (2 works) · Statistics (2 works) · Advanced Graph Neural Networks (1 work)