Event models from policy descriptions

2016 ◽  
Author(s):  
Nandan Parameswaran ◽  
Piyush Maheshwari
Keyword(s):  
Event Models

Cancer classification from serial analysis of gene expression with event models

2007 ◽  
Vol 29 (1) ◽  
pp. 35-46 ◽  
Author(s):  
Xin Jin ◽  
Anbang Xu ◽  
Rongfang Bie
Keyword(s):  
Gene Expression ◽  
Cancer Classification ◽  
Event Models

Complex event models for automotive embedded systems

2021 ◽  
pp. 102343
Author(s):  
Lukas Krawczyk ◽  
Mahmoud Bazzal ◽  
Harald Mackamul ◽  
Raphael Weber ◽  
Carsten Wolff
Keyword(s):  
Embedded Systems ◽  
Event Models

A comparison of estimators to evaluate the discriminatory power of time-to-event models

2012 ◽  
Vol 31 (23) ◽  
pp. 2588-2609 ◽  
Author(s):  
Matthias Schmid ◽  
Sergej Potapov
Keyword(s):  
Discriminatory Power ◽  
Time To Event ◽  
Event Models

scholarly journals Quantifying the predictive accuracy of time-to-event models in the presence of competing risks

2011 ◽  
Vol 53 (1) ◽  
pp. 88-112 ◽  
Author(s):  
Rotraut Schoop ◽  
Jan Beyersmann ◽  
Martin Schumacher ◽  
Harald Binder
Keyword(s):  
Competing Risks ◽  
Predictive Accuracy ◽  
Time To Event ◽  
Event Models

scholarly journals State Variable Effects in Graphical Event Models

2020 ◽  
Author(s):  
Debarun Bhattacharjya ◽  
Dharmashankar Subramanian ◽  
Tian Gao
Keyword(s):  
Real World ◽  
Continuous Time ◽  
Point Processes ◽  
Temporal Dynamics ◽  
Search Algorithm ◽  
State Variables ◽  
Discrete State ◽  
State Variable ◽  
Event Models ◽  
Real World Datasets

Many real-world domains involve co-evolving relationships between events, such as meals and exercise, and time-varying random variables, such as a patient's blood glucose levels. In this paper, we propose a general framework for modeling joint temporal dynamics involving continuous time transitions of discrete state variables and irregular arrivals of events over the timeline. We show how conditional Markov processes (as represented by continuous time Bayesian networks) and multivariate point processes (as represented by graphical event models) are among various processes that are covered by the framework. We introduce and compare two simple and interpretable yet practical joint models within the framework with relevant baselines on simulated and real-world datasets, using a graph search algorithm for learning. The experiments highlight the importance of jointly modeling event arrivals and state variable transitions to better fit joint temporal datasets, and the framework opens up possibilities for models involving even more complex dynamics whenever suitable.

scholarly journals Order-Dependent Event Models for Agent Interactions

2020 ◽  
Author(s):  
Debarun Bhattacharjya ◽  
Tian Gao ◽  
Dharmashankar Subramanian
Keyword(s):  
Arrival Rate ◽  
Event Data ◽  
Political Event ◽  
Instantaneous Rate ◽  
Agent Interactions ◽  
Occurrence Data ◽  
Proposed Model ◽  
Event Models ◽  
Better Than ◽  
Event Occurrence

In multivariate event data, the instantaneous rate of an event's occurrence may be sensitive to the temporal sequence in which other influencing events have occurred in the history. For example, an agent’s actions are typically driven by preceding actions taken by the agent as well as those of other relevant agents in some order. We introduce a novel statistical/causal model for capturing such an order-sensitive historical dependence, where an event’s arrival rate is determined by the order in which its underlying causal events have occurred in the recent past. We propose an algorithm to discover these causal events and learn the most influential orders using time-stamped event occurrence data. We show that the proposed model fits various event datasets involving single as well as multiple agents better than baseline models. We also illustrate potentially useful insights from our proposed model for an analyst during the discovery process through analysis on a real-world political event dataset.

scholarly journals Limited support for the X-linked grandmother hypothesis in pre-industrial Finland

2018 ◽  
Vol 14 (1) ◽  
pp. 20170651 ◽  
Author(s):  
Simon N. Chapman ◽  
Jenni E. Pettay ◽  
Virpi Lummaa ◽  
Mirkka Lahdenperä
Keyword(s):  
X Chromosome ◽  
Limited Support ◽  
Grandmother Hypothesis ◽  
Event Models

The level of kin help often depends on the degree of relatedness between a helper and the helped. In humans, grandmother help is known to increase the survival of grandchildren, though this benefit can differ between maternal grandmothers (MGMs) and paternal grandmothers (PGMs) and between grandsons and granddaughters. The X-linked grandmother hypothesis posits that differential X-chromosome relatedness between grandmothers and their grandchildren is a leading driver of differential grandchild survival between grandmother lineages and grandchild sexes. We tested this hypothesis using time-event models on a large, multigenerational dataset from pre-industrial Finland. We found that the presence of an MGM increases grandson survival more than PGM presence, and that granddaughter survival is higher than that of grandsons in the presence of a PGM. However, there was no support for the key prediction that the presence of PGMs improves granddaughter survival more than that of MGMs, diminishing the overall support for the hypothesis. Our results call for alternative explanations for differences in the effects of maternal and paternal kin to grandchild survival in humans.

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