#### Event Title

Probabilistic Modeling of Self-Motion Perception

#### Location

Diamond 344

#### Start Date

30-4-2015 11:00 AM

#### End Date

30-4-2015 11:55 AM

#### Project Type

Presentation

#### Description

This study focuses on the Bayesian modeling of angular self-motion incorporated with the graphical representation of the individual perception of the acceleration under a centrifuge motion. To this end, we take into consideration multiple individual data and study the phenomenon throughout the entire distribution. Each individual has a likelihood of behaving as described by the set of differential equation of the deterministic model. Instead of determining the self-perception of motion throughout time, we observe the tendency or behavior of our data points relative to angular velocity of the pitch, yawn and roll, given the initial parameters. In order to obtain the results we compute likelihood and probabilities of each data point, an iterative process that follows an appropriate data filtering method and determination of the mean of the next distribution.

#### Faculty Sponsor

Ben Mathes

#### Sponsoring Department

Colby College. Mathematics and Statistics Dept.

#### CLAS Field of Study

Natural Sciences

#### Event Website

http://www.colby.edu/clas

#### ID

1509

Probabilistic Modeling of Self-Motion Perception

Diamond 344

This study focuses on the Bayesian modeling of angular self-motion incorporated with the graphical representation of the individual perception of the acceleration under a centrifuge motion. To this end, we take into consideration multiple individual data and study the phenomenon throughout the entire distribution. Each individual has a likelihood of behaving as described by the set of differential equation of the deterministic model. Instead of determining the self-perception of motion throughout time, we observe the tendency or behavior of our data points relative to angular velocity of the pitch, yawn and roll, given the initial parameters. In order to obtain the results we compute likelihood and probabilities of each data point, an iterative process that follows an appropriate data filtering method and determination of the mean of the next distribution.

http://digitalcommons.colby.edu/clas/2015/program/344