Modelling discrete event sequences as state transition diagrams

1997 ◽  
pp. 573-584 ◽  
Author(s):  
Adele E. Howe ◽  
Gabriel Somlo
Keyword(s):  
State Transition ◽  
Discrete Event ◽  
Event Sequences

scholarly journals Stochastic DES Fault Diagnosis with Coloured Interpreted Petri Nets

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Doyra Mariela Muñoz ◽  
Antonio Correcher ◽  
Emilio García ◽  
Francisco Morant
Keyword(s):  
Petri Net ◽  
Discrete Event Systems ◽  
Necessary Conditions ◽  
Learning Algorithm ◽  
Discrete Event ◽  
Event Sequences ◽  
Normal Behavior ◽  
Sufficient And Necessary Conditions ◽  
Stochastic Discrete Event Systems ◽  
Identification Stage

This proposal presents an online method to detect and isolate faults in stochastic discrete event systems without previous model. A coloured timed interpreted Petri Net generates the normal behavior language after an identification stage. The next step is fault detection that is carried out by comparing the observed event sequences with the expected event sequences. Once a new fault is detected, a learning algorithm changes the structure of the diagnoser, so it is able to learn new fault languages. Moreover, the diagnoser includes timed events to represent and diagnose stochastic languages. Finally, this paper proposes a detectability condition for stochastic DES and the sufficient and necessary conditions are proved.

scholarly journals Dynamic arousal signals construct memories of time and events

2019 ◽  
Author(s):  
David Clewett ◽  
Camille Gasser ◽  
Lila Davachi
Keyword(s):  
Cognitive Processes ◽  
Discrete Event ◽  
Sudden Change ◽  
Temporal Distance ◽  
Recent Event ◽  
Event Sequences ◽  
Cognitive Components ◽  
Past Experiences ◽  
Event Boundary ◽  
The Brain

AbstractEveryday life unfolds continuously, yet we tend to remember past experiences as discrete event sequences or episodes. Although this phenomenon has been well documented, the brain mechanisms that support the transformation of continuous experience into memorable episodes remain unknown. Here we show that a sudden change in context, or ‘event boundary’, elicits a burst of autonomic arousal, as indexed by pupil dilation. These boundaries during dynamic experience also led to the segmentation of adjacent episodes in later memory, evidenced by changes in memory for the timing, order, and perceptual details of recent event sequences. Critically, we find that distinct cognitive components of this pupil response were associated with both subjective (temporal distance judgements) and objective (temporal order discrimination) measures of episodic memory, suggesting that multiple arousal-mediated cognitive processes help construct meaningful mnemonic events. Together, these findings reveal that arousal processes may play a fundamental role in everyday memory organization.

scholarly journals Temporal and Rate Coding for Discrete Event Sequences in the Hippocampus

Neuron ◽  
2017 ◽  
Vol 94 (6) ◽  
pp. 1248-1262.e4 ◽  
Author(s):  
Satoshi Terada ◽  
Yoshio Sakurai ◽  
Hiroyuki Nakahara ◽  
Shigeyoshi Fujisawa
Keyword(s):  
Discrete Event ◽  
Event Sequences ◽  
Rate Coding

scholarly journals Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation

2021 ◽  
Vol 41 (4) ◽  
pp. 453-464
Author(s):  
John Graves ◽  
Shawn Garbett ◽  
Zilu Zhou ◽  
Jonathan S. Schildcrout ◽  
Josh Peterson
Keyword(s):  
Discrete Time ◽  
Stochastic Models ◽  
State Transition ◽  
Markov Models ◽  
Transition Probabilities ◽  
Discrete Event ◽  
Computation Time ◽  
Quality Adjusted Life Year ◽  
Simulated Patients ◽  
Optimal Decisions

We discuss tradeoffs and errors associated with approaches to modeling health economic decisions. Through an application in pharmacogenomic (PGx) testing to guide drug selection for individuals with a genetic variant, we assessed model accuracy, optimal decisions, and computation time for an identical decision scenario modeled 4 ways: using 1) coupled-time differential equations (DEQ), 2) a cohort-based discrete-time state transition model (MARKOV), 3) an individual discrete-time state transition microsimulation model (MICROSIM), and 4) discrete event simulation (DES). Relative to DEQ, the net monetary benefit for PGx testing (v. a reference strategy of no testing) based on MARKOV with rate-to-probability conversions using commonly used formulas resulted in different optimal decisions. MARKOV was nearly identical to DEQ when transition probabilities were embedded using a transition intensity matrix. Among stochastic models, DES model outputs converged to DEQ with substantially fewer simulated patients (1 million) v. MICROSIM (1 billion). Overall, properly embedded Markov models provided the most favorable mix of accuracy and runtime but introduced additional complexity for calculating cost and quality-adjusted life year outcomes due to the inclusion of “jumpover” states after proper embedding of transition probabilities. Among stochastic models, DES offered the most favorable mix of accuracy, reliability, and speed.

scholarly journals Comparison of Decision Modeling Approaches for Health Technology and Policy Evaluation

2020 ◽  
Author(s):  
John Graves ◽  
Shawn Garbett ◽  
Zilu Zhou ◽  
Jonathan S. Schildcrout ◽  
Josh Peterson
Keyword(s):  
Discrete Time ◽  
Stochastic Models ◽  
State Transition ◽  
Markov Models ◽  
Transition Probabilities ◽  
Discrete Event ◽  
Computation Time ◽  
Quality Adjusted Life Year ◽  
Simulated Patients ◽  
Optimal Decisions

ABSTRACTWe discuss tradeoffs and errors associated with approaches to modeling health economic decisions. Through an application in pharmacogenomic (PGx) testing to guide drug selection for individuals with a genetic variant, we assessed model accuracy, optimal decisions and computation time for an identical decision scenario modeled four ways: using (1) coupled-time differential equations [DEQ]; (2) a cohort-based discrete-time state transition model [MARKOV]; (3) an individual discrete-time state transition microsimulation model [MICROSIM]; and (4) discrete event simulation [DES]. Relative to DEQ, the Net Monetary Benefit for PGx testing (vs. a reference strategy of no testing) based on MARKOV with rate-to-probability conversions using commonly used formulas resulted in different optimal decisions. MARKOV was nearly identical to DEQ when transition probabilities were embedded using a transition intensity matrix. Among stochastic models, DES model outputs converged to DEQ with substantially fewer simulated patients (1 million) vs. MICROSIM (1 billion). Overall, properly embedded Markov models provided the most favorable mix of accuracy and run-time, but introduced additional complexity for calculating cost and quality-adjusted life year outcomes due to the inclusion of “jumpover” states after proper embedding of transition probabilities. Among stochastic models, DES offered the most favorable mix of accuracy, reliability, and speed.

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