Hi, Guys.

I have a question about building linear model

I’ve read some examples about linear model with continuos predictors.

But I don’t know how to set prior distributions on the binary(boolean) or categorcal or ordinal variables.

Let’s say I have dataframe x_train and y_train(IQ score).

x_train consists of 4 variables age, sex, self-esteem, favorite-fruit.

age is continuous variable, sex is binary, self-esteem is ordinal(1 to 5), fruit is categorical variable(1 to 6).

How should I build a linear model with non informative or weak priors?

```
@model function lin_reg(x, y)
#priors
α ~ Normal(mean(y), 10) # intercept
σ ~ Exponential(1) #sigma
beta1 ~ Normal(0, 10) #age
beta2 ~ Bernoulli(0.5) # sex
beta3 ~ DiscreteUniform(1, 5) # self-esteem
beta4 ~ DiscreteUniform(1, 6) # fruit
μ = α .+ beta1 * x[:, :age] .+ beta2 * x[:, :sex] .+ beta3 * x[:, :esteem] .+ beta4 * x[:, :fruit]
y ~ MvNormal(μ, σ)
end
```

Thank you!