# Posts by Nathaniel Forde

## Bayesian Non-parametric Causal Inference

- 26 January 2024

There are few claims stronger than the assertion of a causal relationship and few claims more contestable. A naive world model - rich with tenuous connections and non-sequiter implications is characteristic of conspiracy theory and idiocy. On the other hand, a refined and detailed knowledge of cause and effect characterised by clear expectations, plausible connections and compelling counterfactuals, will steer you well through the buzzing, blooming confusion of the world.

## Frailty and Survival Regression Models

- 26 November 2023

This notebook uses libraries that are not PyMC dependencies and therefore need to be installed specifically to run this notebook. Open the dropdown below for extra guidance.

## Discrete Choice and Random Utility Models

- 26 June 2023

This notebook uses libraries that are not PyMC dependencies and therefore need to be installed specifically to run this notebook. Open the dropdown below for extra guidance.

## Regression Models with Ordered Categorical Outcomes

- 26 April 2023

Like many areas of statistics the language of survey data comes with an overloaded vocabulary. When discussing survey design you will often hear about the contrast between *design* based and *model* based approaches to (i) sampling strategies and (ii) statistical inference on the associated data. We won’t wade into the details about different sample strategies such as: simple random sampling, cluster random sampling or stratified random sampling using population weighting schemes. The literature on each of these is vast, but in this notebook we’ll talk about when any why it’s useful to apply model driven statistical inference to Likert scaled survey response data and other kinds of ordered categorical data.

## Longitudinal Models of Change

- 26 April 2023

The study of change involves simultaneously analysing the individual trajectories of change and abstracting over the set of individuals studied to extract broader insight about the nature of the change in question. As such it’s easy to lose sight of the forest for the focus on the trees. In this example we’ll demonstrate some of the subtleties of using hierarchical bayesian models to study the change within a population of individuals - moving from the *within individual* view to the *between/cross individuals* perspective.

## Bayesian Missing Data Imputation

- 26 February 2023

Duplicate implicit target name: “bayesian missing data imputation”.

## Reliability Statistics and Predictive Calibration

- 26 January 2023

Duplicate implicit target name: “reliability statistics and predictive calibration”.

## Bayesian Vector Autoregressive Models

- 26 November 2022

Duplicate implicit target name: “bayesian vector autoregressive models”.

## Forecasting with Structural AR Timeseries

- 20 October 2022

Bayesian structural timeseries models are an interesting way to learn about the structure inherent in any observed timeseries data. It also gives us the ability to project forward the implied predictive distribution granting us another view on forecasting problems. We can treat the learned characteristics of the timeseries data observed to-date as informative about the structure of the unrealised future state of the same measure.