Intracellular transport: Inferring properties of molecular motors through rigor motor experiments and Bayesian inference. Part 1.

Bayes’s Theorem yields the conditional probability of an event A given event B based on prior knowledge about related events.  When considering distributions, Bayes’s Theorem can be used to describe the distribution of model parameters given observed data.  In practice, this requires calculating a likelihood function and making a particular assumption on the parameters from the outset, called a prior distribution.  In this talk, I discuss a model for a rigor motor experiment and create an approximate posterior distribution using Bayes’s Theorem. Then, using a Gibbs Sampler I will sample from the posterior distribution to obtain parameter estimates for my model.