Bayesian inference of particle motion and dynamics from single particle tracking and fluorescence correlation spectroscopy

A non-transient computer-readable medium carrying one or more sequences of instructions, wherein execution of the one or more sequences of instructions by one or more processors causes an apparatus to perform at least the following:

• determine a plurality of models for motion of particles in a biological sample, each model including a corresponding set of one or more parameters;
• obtain measured data based on measurements at one or more voxels of an imaging system sensitive to motion of particles in the biological sample;
• determine noise correlation of the measured data;
• determine, based at least in part on the noise correlation, a marginal likelihood of the measured data for each model of the plurality of models;
• determine a relative probability for each model of the plurality of models based on the marginal likelihood;
• and
• determine, based at least in part on the relative probability for each model, a value for a parameter of the set of one or more parameters corresponding to a selected model of the plurality of models.

An apparatus comprising:

• at least one processor;
• and
• at least one memory including one or more sequences of instructions,
• the at least one memory and the one or more sequences of instructions configured to, with the at least one processor, cause the apparatus to perform at least the following,
• determine a plurality of models for motion of particles in a biological sample, each model including a corresponding set of one or more parameters;
• obtain measured data based on measurements at one or more voxels of an imaging system sensitive to motion of particles in the biological sample;
• determine noise correlation of the measured data;
• determine, based at least in part on the noise correlation, a marginal likelihood of the measured data for each model of the plurality of models;
• determine a relative probability for each model of the plurality of models based on the marginal likelihood;
• and
• determine, based at least in part on the relative probability for each model, a value for a parameter of the set of one or more parameters corresponding to a selected model of the plurality of models.