The curve of the probability density function for the exponential distribution has a sharp bend at $$x=0$$. The probability density function is given by $$p(x;\lambda)=\lambda \mathbf{1}_{x\geq0} e^{-\lambda x}$$, where $$\mathbf{1}_{x\geq0}$$ is an indicator function that assigns zero probability for all negative values of $$x$$.

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Common probability distributions include Normal Distribution, Multinoulli Distribution,  Bernoulli Probability Distribution, Exponential Distribution, and Laplace Distribution. 

Common Probability Distributions

A probability density function characterizes continuous random variables by assigning a probability density, rather than an exact probability, to possible values. Since the exact probability of any single continuous value is zero, determining the probability that the variable falls within a specific interval requires calculating the definite integral of the density function over that interval.

Probability Density Function

Goodfellow, I., Bengio, Y., & Courville, A. (2016). $\mathit{Deep \ Learning.}$ MIT Press. Retrieved from [www.deeplearningbook.org](https://www.deeplearningbook.org) 

Deep Learning

Bernoulli distribution is a distribution over a single binary variable. It can only have results in 0 or 1. The sum of the probability of 0  and the probability of 1 is equal to 1. If we denote the probability of 0 as $\sigma$, Bernoulli distribution then has a mean of $\sigma$ and a variance of  $\sigma * (1- \sigma)$.

Bernoulli Distribution

Laplace distribution also called double exponential distribution has a sharp bend at random values of  $\mu$

$$Laplace(x;\mu,\gamma)= \frac{1}{2 \gamma} e^{\frac{ - \lvert x - \mu \rvert}{\gamma}}$$


Laplace Distribution

Empirical distribution  showcases the empirical frequency associated with a point/sample in the dataset/set of samples.

$\hat{p}(x) = \frac{1}{m} \sum_{i=1}^{m} \delta (x-x^i)$

The dirac delta function is used to describe the empirical distribution of continuous random variables.

Multinoulli distribution is sufficient to explain the empirical distribution of discrete random variables.


Empirical Distribution

Mixture distribution is a combination of several other probability distributions.

Mixture Distribution


There are some functions used while describing probability distribution in deep learning models they are
- Logistic Sigmoid
- Softplus Function


Properties of Common Functions

A prior probability distribution is a probability distribution over the parameters of a model that encodes our beliefs about what models are reasonable, before we have seen any data.

prior probability distribution

A normal distribution, also known as a Gaussian distribution, is a continuous probability distribution defined by its mean $$\mu$$ and variance $$\sigma^2$$ (where $$\sigma$$ is the standard deviation). The probability density function is given by the formula: $$p(x) = \frac{1}{\sqrt{2 \pi \sigma^2}} \exp\left(-\frac{1}{2 \sigma^2} (x - \mu)^2ight)$$. Changing the mean $$\mu$$ corresponds to a shift of the distribution along the $$x$$-axis, while increasing the variance $$\sigma^2$$ spreads the distribution out and lowers its peak.

Gaussian (Normal) Distribution

To specify a probability distribution where all mass is concentrated at a single point, the probability density function is defined using the Dirac delta function, $$\delta(x)$$. The Dirac delta function evaluates to zero everywhere except at $$x=0$$, and it integrates to one. A Dirac distribution centered at $$\mu$$ is given by the formula $$p(x) = \delta(x-\mu)$$. This indicates that $$p(x)$$ is infinitely large at $$x=\mu$$ and has a unit area.

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