The Volatility of Data Space: Topology Oriented Sensitivity Analysis

Despite the difference among specific methods, existing Sensitivity Analysis (SA) technologies are all value-based, that is, the uncertainties in the model input and output are quantified as changes of values. This paradigm provides only limited insight into the nature of models and the modeled systems. In addition to the value of data, a potentially richer information about the model lies in the topological difference between pre-model data space and post-model data space. This paper introduces an innovative SA method called Topology Oriented Sensitivity Analysis, which defines sensitivity as the volatility of data space. It extends SA into a deeper level that lies in the topology of data.

• Publication year

  2015

• Venue

  PLoS ONE

• Publication date

  2015-09-14

• Fields of study

  Mathematics, Computer Science, Engineering, Medicine

• Identifiers
  DOI 10.1371/journal.pone.0137591PMID 26368929PMCID 4569382
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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