Is the product of conditional probability distributions a valid distribution? In probabilistic machine learning, the likelihood of the data is usually computed as the product of the individual likelihoods of seeing each data point given the parameters $\theta$. In logistic regression, the likelihood of the data given the parameters $\theta$ is equal to $$P(Y|X,\theta) = \prod_{i=1}^m[\frac{1}{1+e^{-x_i\theta}}]^{y_i}[1-\frac{1}{1+e^{-x_i\theta}}]^{1-y_i}$$ This is just the product of $m$ bernouli distributions. I have two questions.

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*What is the relation between a conditional density distribution and the likelihood that is used in baye's theorem ?

*Is the product of distributions in this setting, bernouli distributions going to result in a valid conditional distribution ?

EDIT
Regarding the first question, suppose I am able to define a joint distribution for $P(Y,\theta|X)$. This is assuming $\theta$ random variable is follows some distribution. Then, given a fixed data $Y$, we can slice the joint distribution at $Y=y$ and obtain the marginal density $P(\theta|Y=data,X)$. Then $argmax_{\theta}P(\theta|Y=data,X)$ represents  the $\theta_{MLE}$. In this case the conditional probability is the likelihood function for $\theta$. Integrating this yields a value of 1. However, if $\theta$ does not follow some distribution then integrating this likelihood is not equal to 1 ?
 A: The entire (Bayesian and classical) analysis of a generalised linear model is conditional on the regressor vector $X=(x_1,\ldots,x_n)$.
The joint distribution of the $Y_i$'s in the logit model
$$p(y|x,\theta) = \prod_{i=1}^m[\frac{1}{1+e^{-x_i\theta}}]^{y_i}[1-\frac{1}{1+e^{-x_i\theta}}]^{1-y_i}$$where
$$y=(y_1,\ldots,y_n)\quad\text{and}\quad x=(x_1,\ldots,x_n)$$
is a valid joint pmf [on the components of $Y$] conditional on the vector $X=(x_1,\ldots,x_n)$, assuming the $Y_i$'s are independent given $X$ and that
$$\mathbb P(Y_i=1|X=x,\theta)=\frac{1}{1+e^{-x_i\theta}}$$
As a joint distribution, it defines a likelihood function
$$\ell(\theta|X,Y)=\prod_{i=1}^m[\frac{1}{1+e^{-x_i\theta}}]^{y_i}[1-\frac{1}{1+e^{-x_i\theta}}]^{1-y_i}$$
that can be used in a Bayesian analysis.
As a function of $\theta$, the likelihood is not a pdf, it does not integrate to one except in some specific cases (not including the logit model).  The same applies when given a prior $\pi(\theta)$ one considers the product $\ell(\theta)\pi(\theta)$: it does not integrate to one. The joint distribution of $\theta$ and $Y$ is
$$p(y|\theta)\pi(\theta)$$
which integrates to one in $(y,\theta)$ and the conditional distribution of $\theta$ given $Y=y$ (and $X$) is
$$\dfrac{p(y|\theta)\pi(\theta)}{\int_\Theta p(y|\eta)\pi(\eta)\,\text{d}\eta}$$
which integrates to one in $\theta$. The marginal
$$\int_\Theta p(y|\eta)\pi(\eta)\,\text{d}\eta$$
integrates to one in $y$ [except that it is a summation since $Y$ is discrete].
