The Burg Method and Yule-Walker Method blocks return similar results for large frame sizes.

This table compares the features of the Burg Method block to the Covariance Method, Modified Covariance Method, and the Yule-Walker Method blocks.

| Burg | Covariance | Modified Covariance | Yule-Walker |
---|

**Characteristics**
| Does not apply window to data | Does not apply window to data | Does not apply window to data | Applies window to data |

Minimizes the forward and backward prediction errors in the least squares sense, with the AR coefficients constrained to satisfy the L-D recursion | Minimizes the forward prediction error in the least squares sense | Minimizes the forward and backward prediction errors in the least squares sense | Minimizes the forward prediction error in the least squares sense (also called *autocorrelation method*) |

**Advantages**
| High resolution for short data records | Better resolution than Yule-Walker for short data records (more accurate
estimates) | High resolution for short data records | Performs as well as other methods for large data records |

Always produces a stable model | Able to extract frequencies from data consisting of *p*
or more pure sinusoids | Able to extract frequencies from data consisting of p or more pure sinusoids | Always produces a stable model |

Does not suffer spectral line-splitting |

**Disadvantages**
| Peak locations highly dependent on initial phase | Can produce unstable models | Can produce unstable models | Performs relatively poorly for short data records |

Can suffer spectral line-splitting for sinusoids in noise, or when order is very
large | Frequency bias for estimates of sinusoids in noise | Peak locations slightly dependent on initial phase | Frequency bias for estimates of sinusoids in noise |

Frequency bias for estimates of sinusoids in noise | Minor frequency bias for estimates of sinusoids in noise |

**Conditions for Nonsingularity**
| | Order must be less than or equal to 1/2 the input frame size | Order must be less than or equal to 2/3 the input frame size | Because of the biased estimate, the autocorrelation matrix is guaranteed to be positive-definite, hence nonsingular |