- Bayesian Regression - Inference Algorithms (Part 2) Bayesian Regression - Inference Algorithms (Part 2) — Pyro Tutorials 1.8.2 documentation

When running the SVI with the below guide, it is mentioned it is `DiagNormal`

.

```
def guide(is_cont_africa, ruggedness, log_gdp):
a_loc = pyro.param('a_loc', torch.tensor(0.))
a_scale = pyro.param('a_scale', torch.tensor(1.),
constraint=constraints.positive)
sigma_loc = pyro.param('sigma_loc', torch.tensor(1.),
constraint=constraints.positive)
weights_loc = pyro.param('weights_loc', torch.randn(3))
weights_scale = pyro.param('weights_scale', torch.ones(3),
constraint=constraints.positive)
a = pyro.sample("a", dist.Normal(a_loc, a_scale))
b_a = pyro.sample("bA", dist.Normal(weights_loc[0], weights_scale[0]))
b_r = pyro.sample("bR", dist.Normal(weights_loc[1], weights_scale[1]))
b_ar = pyro.sample("bAR", dist.Normal(weights_loc[2], weights_scale[2]))
sigma = pyro.sample("sigma", dist.Normal(sigma_loc, torch.tensor(0.05)))
mean = a + b_a * is_cont_africa + b_r * ruggedness + b_ar * is_cont_africa * ruggedness
```

Can someone please explain, how we came to the conclusion? I am new to the domain.