Bayesian Inference (BI) is the process of making conclusions from evidence acquired through observations of a given signal, which are formed upon the basis of Bayesian probability principals. BI provides a means to solving practical signal processing applications such as signal prediction, or restoration.

Bayesian inference with regards to parameter estimation aims to provide the best possible approximation of a parameter \theta vector from the information given in an observation y. The best estimate is found by minimizing the risk function, which is proportional to the product of the prior PDF of \theta, the likelihood that the observation signal y occurs given \theta, and the cost of error function, which is often based on the minimum mean square error criteria.

Risk(\hat{\theta } ) = \int \int Cost(\hat{\theta}\ ,\theta) f_{Y|\theta}(y|\theta) f{_{\theta}}(\theta) dy d\theta

Consider an example where it is desired that interpolation of a segment of lost samples of a signal given N known samples. For this case, it is assumed y is modeled by an autoregressive (AR) process.
y is the observation signal vector: y = X\Theta + e + n
\Theta is the AR parameter vector which is to be estimated
e is random input from the AR model and n is random noise
X is the clean signal vector
The estimate for \Theta can be averaged over the entire observation samples N. As N becomes larger, \hat{\Theta} ̂becomes closer to the actual value of \Theta. A challenge to this problem is that e is purely random, and cannot be estimated in the same way.

interpolator block