Probabilistic-Programming

It is currently the end of week five of Google Summer of Code 2023. According to the original deliverables table outlined in my proposal, the goal was to have opened a draft PR for the core functionality of the plot_comparisons. Subsequently, week six and seven were to be spent further developing the plot_comparisons function, and writing tests and a demo notebook for the documentation, respectively. However, at the end of week five, I have a PR open with the majority of the functionality that marginaleffects has. In addition, I also exposed the comparisons function, added tests (which can and will be improved), and have started on documentation. ...

A variant of the Metropolis-Hastings (MH) algorithm that uses clever proposals and is therefore more efficient (you can get a good approximate of the posterior with far fewer samples) is Gibbs sampling. A problem with MH is the need to choose the proposal distribution, and the fact that the acceptance rate may be low. The improvement arises from adaptive proposals in which the distribution of proposed parameter values adjusts itself intelligently, depending upon the parameter values at the moment. This dependence upon the parameters at that moment is an exploitation of conditional independence properties of a graphical model to automatically create a good proposal, with acceptance probability equal to one. ...

Main Idea Metropolis-Hastings (MH) is one of the simplest kinds of MCMC algorithms. The idea with MH is that at each step, we propose to move from the current state $x$ to a new state $x’$ with probability $q(x’|x)$, where $q$ is the proposal distribution. The user is free to choose the proposal distribution and the choice of the proposal is dependent on the form of the target distribution. Once a proposal has been made to move to $x’$, we then decide whether to accept or reject the proposal according to some rule. If the proposal is accepted, the new state is $x’$, else the new state is the same as the current state $x$. ...

Inference In the probabilistic approach to machine learning, all unknown quantities—predictions about the future, hidden states of a system, or parameters of a model—are treated as random variables, and endowed with probability distributions. The process of inference corresponds to computing the posterior distribution over these quantities, conditioning on whatever data is available. Given that the posterior is a probability distribution, we can draw samples from it. The samples in this case are parameter values. The Bayesian formalism treats parameter distributions as the degrees of relative plausibility, i.e., if this parameter is chosen, how likely is the data to have arisen? We use Bayes’ rule for this process of inference. Let $h$ represent the uknown variables and $D$ the known variables, i.e., the data. Given a likelihood $p(D|h)$ and a prior $p(h)$, we can compute the posterior $p(h|D)$ using Bayes’ rule: ...

We don’t know the real posterior so we are going to choose a distribution $Q(\theta)$ from a family of distributions $Q^*$ that are easy to work with and parameterized by $\theta$. The approximate distribution should be as close as possible to the true posterior. This closeness is measured using KL-Divergence. If we have the joint $p(x, z)$ where $x$ is some observed data, the goal is to perform inference: given what we have observed, what can we infer about the latent states?, i.e , we want the posterior. ...