Sökning: "Causal Discovery"

Visar resultat 1 - 5 av 14 uppsatser innehållade orden Causal Discovery.

1. 1. Causal Discovery for Time Series : Based on Continuous Optimization

   Uppsats för yrkesexamina på avancerad nivå, Luleå tekniska universitet/Institutionen för system- och rymdteknik

   Författare :Ali Nouri; [2023]
   Nyckelord :;

   Sammanfattning : Causal discovery is an important field of study that seeks to understand the underlying relationships between variables in a system. The goal of causal discovery is to discover the causal relationships from observational data and determine the direction of influence between variables. LÄS MER

3. 2. Finding Causal Relationships Among Metrics In A Cloud-Native Environment

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Suresh Rishi Nandan; [2023]
   Nyckelord :Causality; Causal Discovery; Bayesian Network; Conditional Independence; Partial Correlation; Ensemble Causal Discovery; Anomaly Detection; Causal Graphs; Causality; Causal Discovery; Bayesian Network; Conditional Indeberoende; partiell korrelation; Ensemble Causal Discovery; Anomali Detektion; kausala grafer;

   Sammanfattning : Automatic Root Cause Analysis (RCA) systems aim to streamline the process of identifying the underlying cause of software failures in complex cloud-native environments. These systems employ graph-like structures to represent causal relationships between different components of a software application. LÄS MER

4. 3. Towards Causal Discovery on EHR data : Evaluation of current Causal Discovery methods on the MIMIC-IV data set

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Pontus Olausson; [2022]
   Nyckelord :Causal Discovery; Time-Series; MIMIC-IV; EHR; Kasual Upptäckt; Tidsserier; MIMIC-IV; elektroniska patientjournaler;

   Sammanfattning : Causal discovery is the problem of learning causal relationships between variables from a set of data. One interesting area of use for causal discovery is the health care domain, where application could help facilitate a better understanding of disease and treatment mechanisms. LÄS MER

5. 4. Causal discovery in conditional stationary time-series data : Towards causal discovery in videos

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Carles Balsells Rodas; [2021]
   Nyckelord :Causality; Causal discovery; Neural networks; Graph neural network; Time series; Non-stationary; Orsakssamband; Kausal upptäckt; Neurala nätverk; Diagram Neurala nätverk; Tidsföljder; Icke-stationär;

   Sammanfattning : Performing causal reasoning in a scene is an inherent mechanism in human cognition; however, the majority of approaches in the causality literature aiming for this task still consider constrained scenarios, such as simple physical systems or stationary time-series data. In this work we aim for causal discovery in videos concerning realistic scenarios. LÄS MER

7. 5. Interventions for Identifying Context-Specific Causal Structures

   Master-uppsats, KTH/Matematik (Avd.)

   Författare :Georgios-Nikolaos Karelas; [2021]
   Nyckelord :causality; game theory; mathematics; kausalitet; spelteori; matematik;

   Sammanfattning : The problem of causal discovery is to learn the true causal relations among a system of random variables based on the available data. Learning the true causal structure of p variables can sometimes be difficult, but it is crucial in many fields of science, such as biology, sociology and artificial intelligence. LÄS MER