# Bayesian Linear Regression

"All models are wrong but some are useful"

George Box (Box, 1976)

This tutorial begins with a very provocative quote from the statistician George Box (figure below) on statistical models. Yes, all models are somehow wrong. But they are very useful. The idea is that the reality is too complex for us to understand when analyzing it in a naked and raw way. We need to somehow simplify it into individual components and analyze their relationships. But there is a danger here: any simplification of reality promotes loss of information in some way. Therefore, we always have a delicate balance between simplifications of reality through models and the inherent loss of information. Now you ask me: "how are they useful?" Imagine that you are in total darkness and you have a very powerful flashlight but with a narrow beam. Are you going to throw the flashlight away because it can't light everything around you and stay in the dark? You must use the flashlight to aim at interesting places in the darkness in order to illuminate them. You will never find a flashlight that illuminates everything with the clarity you need to analyze all the fine details of reality. Just as you will never find a unique model that will explain the whole reality around you. You need different flashlights just like you need different models. Without them you will be in total darkness. George Box

## Linear Regression

Let's talk about a class of model known as linear regression. The idea here is to model a continuous dependent variable with a linear combination of independent variables.

$\mathbf{y} = \alpha + \mathbf{X} \boldsymbol{\beta} + \epsilon$

where:

• $\mathbf{y}$ – dependent variable

• $\alpha$ – intercept

• $\boldsymbol{\beta}$ – coefficient vector

• $\mathbf{X}$ – data matrix

• $\epsilon$ – model error

To estimate the $\boldsymbol{\beta}$ coefficients we use a Gaussian/normal likelihood function. Mathematically the Bayesian regression model is:

\begin{aligned} \mathbf{y} &\sim \text{Normal}\left( \alpha + \mathbf{X} \cdot \boldsymbol{\beta}, \sigma \right) \\ \alpha &\sim \text{Normal}(\mu_\alpha, \sigma_\alpha) \\ \boldsymbol{\beta} &\sim \text{Normal}(\mu_{\boldsymbol{\beta}}, \sigma_{\boldsymbol{\beta}}) \\ \sigma &\sim \text{Exponential}(\lambda_\sigma) \end{aligned}

Here we see that the likelihood function $P(\mathbf{y} \mid \boldsymbol{\theta})$ is a normal distribution in which $\mathbf{y}$ depends on the parameters of the model $\alpha$ and $\boldsymbol{\beta}$, in addition to having an error $\sigma$. We condition $\mathbf{y}$ onto the observed data $\mathbf{X}$ by inserting $\alpha + \mathbf{X} \cdot \boldsymbol{\beta}$ as the linear predictor of the model (the mean $\mu$ parameter of the model's Normal likelihood function, and $\sigma$ is the variance parameter). What remains is to specify which are the priors of the model parameters:

• Prior Distribution of $\alpha$ – Knowledge we possess regarding the model's intercept.

• Prior Distribution of $\boldsymbol{\beta}$ – Knowledge we possess regarding the model's independent variables' coefficients.

• Prior Distribution of $\sigma$ – Knowledge we possess regarding the model's error. Important that the error can only be positive. In addition, it is intuitive to place a distribution that gives greater weight to values close to zero, but that also allows values that are far from zero, so a distribution with a long tail is welcome. Candidate distributions are $\text{Exponential}$ which is only supported on positive real numbers (so it solves the question of negative errors) or $\text{Cauchy}^+$ truncated to only positive numbers (remembering that the distribution Cauchy is Student's $t$ with degrees of freedom $\nu = 1$).

Our goal is to instantiate a linear regression with the observed data ($\mathbf{y}$ and $\mathbf{X}$) and find the posterior distribution of our model's parameters of interest ($\alpha$ and $\boldsymbol{\beta}$). This means to find the full posterior distribution of:

$P(\boldsymbol{\theta} \mid \mathbf{y}) = P(\alpha, \boldsymbol{\beta}, \sigma \mid \mathbf{y})$

This is easily accomplished with Turing:

using Turing
using LinearAlgebra: I
using Statistics: mean, std
using Random: seed!
seed!(123)

@model function linreg(X, y; predictors=size(X, 2))
#priors
α ~ Normal(mean(y), 2.5 * std(y))
β ~ filldist(TDist(3), predictors)
σ ~ Exponential(1)

#likelihood
return y ~ MvNormal(α .+ X * β, σ^2 * I)
end;

Here I am specifying very weakly informative priors:

• $\alpha \sim \text{Normal}(\bar{\mathbf{y}}, 2.5 \cdot \sigma_{\mathbf{y}})$ – This means a normal distribution centered on y's mean with a standard deviation 2.5 times the standard deviation of y. That prior should with ease cover all possible values of $\alpha$. Remember that the normal distribution has support over all the real number line $\in (-\infty, +\infty)$.

• $\boldsymbol{\beta} \sim \text{Student-}t(0,1,3)$ – The predictors all have a prior distribution of a Student-$t$ distribution centered on 0 with variance 1 and degrees of freedom $\nu = 3$. That wide-tailed $t$ distribution will cover all possible values for our coefficients. Remember the Student-$t$ also has support over all the real number line $\in (-\infty, +\infty)$. Also the filldist() is a nice Turing's function which takes any univariate or multivariate distribution and returns another distribution that repeats the input distribution.

• $\sigma \sim \text{Exponential}(1)$ – A wide-tailed-positive-only distribution perfectly suited for our model's error.

Also, we are using the MvNormal construction where we specify both a vector of means (first positional argument) and a covariance matrix (second positional argument). Regarding the covariance matrix σ^2 * I, it uses the model's errors σ, here parameterized as a standard deviation, squares it to produce a variance paramaterization, and multiplies by I, which is Julia's LinearAlgebra standard module implementation to represent an identity matrix of any size.

## Example - Children's IQ Score

For our example, I will use a famous dataset called kidiq (Gelman & Hill, 2007), which is data from a survey of adult American women and their respective children. Dated from 2007, it has 434 observations and 4 variables:

• kid_score: child's IQ

• mom_hs: binary/dummy (0 or 1) if the child's mother has a high school diploma

• mom_iq: mother's IQ

• mom_age: mother's age

Ok let's read our data with CSV.jl and output into a DataFrame from DataFrames.jl:

using DataFrames
using CSV
using HTTP

url = "https://raw.githubusercontent.com/storopoli/Bayesian-Julia/master/datasets/kidiq.csv"
describe(kidiq)
4×7 DataFrame
Row │ variable   mean        min      median   max      nmissing  eltype
│ Symbol     Float64     Real     Float64  Real     Int64     DataType
─────┼──────────────────────────────────────────────────────────────────────
1 │ kid_score   86.7972    20       90.0     144             0  Int64
2 │ mom_hs       0.785714   0        1.0       1             0  Int64
3 │ mom_iq     100.0       71.0374  97.9153  138.893         0  Float64
4 │ mom_age     22.7857    17       23.0      29             0  Int64

As you can see from the describe() output, the mean children's IQ is around 87 while the mother's is 100. Also the mother's range from 17 to 29 years with mean of around 23 years old. Finally, note that 79% of mothers have a high school diploma.

Now let's us instantiate our model with the data:

X = Matrix(select(kidiq, Not(:kid_score)))
y = kidiq[:, :kid_score]
model = linreg(X, y);

And, finally, we will sample from the Turing model. We will be using the default NUTS() sampler with 1_000 samples, but now we will sample from 4 Markov chains using multiple threads MCMCThreads():

chain = sample(model, NUTS(), MCMCThreads(), 1_000, 4)
summarystats(chain)
Summary Statistics
parameters      mean       std   naive_se      mcse         ess      rhat   ess_per_sec
Symbol   Float64   Float64    Float64   Float64     Float64   Float64       Float64

α   21.7281    8.5602     0.1353    0.1793   1754.8189    1.0013       42.8109
β    2.0477    1.8625     0.0294    0.0381   2359.7982    1.0025       57.5701
β    0.5784    0.0597     0.0009    0.0011   2274.1989    1.0009       55.4818
β    0.2474    0.2997     0.0047    0.0067   1922.3309    1.0035       46.8976
σ   17.8812    0.6055     0.0096    0.0106   3135.4623    0.9995       76.4933


We had no problem with the Markov chains as all the rhat are well below 1.01 (or above 0.99). Our model has an error σ of around 18. So it estimates IQ±9. The intercept α is the basal child's IQ. So each child has 22±9 IQ before we add the coefficients multiplied by the child's independent variables. And from our coefficients $\boldsymbol{\beta}$, we can see that the quantile() tells us the uncertainty around their estimates:

quantile(chain)
Quantiles
parameters      2.5%     25.0%     50.0%     75.0%     97.5%
Symbol   Float64   Float64   Float64   Float64   Float64

α    4.1229   16.1645   21.9888   27.6289   37.9615
β   -0.7027    0.6819    1.7162    3.1249    6.4444
β    0.4608    0.5385    0.5784    0.6204    0.6942
β   -0.3221    0.0417    0.2433    0.4422    0.8427
σ   16.7465   17.4712   17.8665   18.2840   19.1087

• β – first column of X, mom_hs, has 95% credible interval that is all over the place, including zero. So its effect on child's IQ is inconclusive.

• β – second column of X, mom_iq, has a 95% credible interval from 0.46 to 0.69. So we expect that every increase in the mother's IQ is associated with a 0.46 to 0.69 increase in the child's IQ.

• β – third column of X, mom_age, has also 95% credible interval that is all over the place, including zero. Like mom_hs, its effect on child's IQ is inconclusive.

That's how you interpret 95% credible intervals from a quantile() output of a linear regression Chains object.

Box, G. E. P. (1976). Science and Statistics. Journal of the American Statistical Association, 71(356), 791–799. https://doi.org/10.2307/2286841

Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge university press.