Tutorial 1: Sequential Probability Ratio Test

# Tutorial 1: Sequential Probability Ratio Test

This tutorial introduces the *Sequential Probability Ratio Test *between two hypotheses 𝐻𝐿 and 𝐻𝑅 by running simulations of a *Drift Diffusion Model (DDM)*. As independent and identically distributed (*i.i.d*) samples from the true data-generating distribution coming in, we accumulate our evidence linearly until a certain criterion is met before deciding which hypothesis to accept. Two types of stopping criterion/stopping rule will be implemented: after seeing a fixed amount of data, and after the likelihood ratio passes a pre-defined threshold. Due to the noisy nature of observations, there will be a *drifting* term governed by expected mean output and a *diffusion* term governed by observation noise.

Topics covered in this lesson

- Introduction to the Sequential Probability Ratio Test (SPRT)
- Accuracy vs stopping time
- Drift diffusion model (DDM) with fixed thresholds
- Accuracy vs threshold

External Links

Prerequisites

Experience with Python Programming Language

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