# Why prior in MAP could be ignored?

A posterior $$p(\theta\vert x) = \frac{p(x \vert \theta)p(\theta)}{p(x)}$$

Many materials say that since the $$p(x)$$ is a constant, the $$p(x)$$ can be ignored. Thus, $$p(\theta\vert x) \propto p(x \vert \theta)p(\theta)$$

My question is why $$p(x)$$ is a constant and ignored. Is this because even though we don't know the distribution x, there is a corresponding true distribution for $$x$$? So, $$p(x)$$ is a constant (we don't know but already determined) and thus, can be ignored?

• Oh, I made the mistake $p(x)$ is not a prior. Prior is $p(\theta)$. I edited my question! – shashack Mar 1 '19 at 5:33

You are right, $$P(x)$$ is the underlying distribution of data, and it can be assumed constant, simply because it is independent of our modeling practice. However $$P(x)$$ may change gradually over time, this phenomenon is called "distribution shift". For example, the distribution of height in a country may change over years.

Note that "prior" is a reserved word for $$P(\theta)$$ which is the distribution of model parameters in Bayesian modeling. In non-Bayesian modeling there is no notion of "prior". $$P(x|\theta)$$ is called likelihood and $$P(x)=\sum_{\theta}P(x|\theta)P(\theta)$$ is called marginalized likelihood (likelihood marginalized over model parameters).

The maximum a posteriori definition usually goes like this:

$$\arg\max_{\theta} p(\theta|x) = \arg\max_{\theta} \frac{p(x|\theta)\cdot p(\theta)}{p(x)}$$

and given that $$p(x)$$ is independent of $$\theta$$, it's not needed for finding the $$\arg\max_{\theta}$$, then you have

$$\arg\max_{\theta} p(\theta|x) = \arg\max_{\theta} \frac{p(x|\theta)\cdot p(\theta)}{p(x)} = \arg\max_{\theta} p(x|\theta)\cdot p(\theta)$$

Yes. Presumably whatever process 'generates' data produces data that follows some distribution. $$p(x)$$, however, is just a number, and whatever it is, it is a constant w.r.t. $$\theta$$.

I wouldn't call $$p(x)$$ a prior, as it implies there's some posterior probability of the data we compute. We don't; nothing about this process updates belief about the probability of the. "Prior" refers to $$p(\theta)$$.

$$p(x)$$ can't be ignored if you really want the value of $$p(\theta|x)$$. But usually you compute that from the posterior distribution that you will have a closed-form formula for.