IntrinsicTimescales.jl
  • Getting Started
  • Explanation
      • Practice
      • Building the Autocorrelation Function
      • Autocorrelation Windows
      • Ornstein-Uhlenbeck Process as a Generative Model for ACF
      • Dealing with Oscillatory Artifacts using Fourier Transformation
      • Bayesian Estimation of Intrinsic Timescales
    • Theory
  • Tutorials
    • Navigating the Forest of INT Metrics
    • Usage with MNE-Python
    • Usage with FieldTrip
  • Reference
      • Model-Free Timescale Estimation
        • Overview
        • One Timescale Model
        • One Timescale Model with Missing Data
        • One Timescale Model with Oscillations
        • One Timescale Model with Oscillations and Missing Data
        • Model Fitting and Parameters
        • Results
    • API
  • Citations
  • Contributing
  • Developer Documentation
Version
  • Reference
  • Implementation
  • Simulation Based Timescale Estimation
  • One Timescale Model with Oscillations and Missing Data
  • One Timescale Model with Oscillations and Missing Data
GitHub

One Timescale and Oscillation with Missing Data (one_timescale_and_osc_with_missing_model)

Uses the same syntax as one_timescale_model and has the same implementation details (i.e. three priors and three results) as one_timescale_and_osc. We refer the users to the respective documentations. The only difference of one_timescale_and_osc_with_missing_model from one_timescale_and_osc is that missing data points is replaced with NaNs in the generative model, as in one_timescale_with_missing_model.

« One Timescale Model with OscillationsModel Fitting and Parameters »

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