Bayesian methods
analytical methodology that takes advantage of [Bayesian logic](https://en.wikipedia.org/wiki/Bayesian_statistics)
credible intervals
The Bayesian equivalent to a confidence interval, the parameter is likely to fall within this interval with the given percentage
homoscedastic
the variance for each data point is the same for all values
likelihood
the probability distribution of some observed data in terms of model parameters
Markov chain Monte Carlo
a random sampling technique that is used to investigate probability distributions
maximum likelihood estimation
the model and parameters that obtain the maximum of the likelihood function for the data
model dataset
the data that arises from our mathematical model
nested sampling
a approach to estimate the multi-dimension integral that gives the Bayesian evidence
normal distribution
a probability distribution that is often used in the natural sciences to represent real-valued random variables, i.e. experimental measurements, also known as a Gaussian distribution
optimisation algorithm
the process used to *try* and get the best agreement between our model and experimental datasets
parameters
values within our mathematical model that may be changed
posterior distribution
the result of the product of the likelihood and prior probabilities
prior knowledge
what we already know about our system before looking at our data, e.g., from other measurements of underlying physics/chemistry
thinning
the sub-sampling of MCMC chains to remove correlation between samples
walkers
unique samplers in a Markov chain Monte Carlo sampling