06/05/2024: 1:45 PM - 2:10 PM EDT

Special Event

Special Event

The National Agricultural Statistics Service (NASS)

of the United States Department of Agriculture

(USDA) uses High-Order Markov

Chains (HOMC) to analyze crop rotation patterns

over time and project future crop-specific

planting. However, HOMCs often face issues with

sparsity and identifiability due to the representation

of categorical data as indicator variables. As the

number of HOMCs needed for analysis increases, the

parametric space's dimension grows exponentially.

Parsimonious representations reduce the number

of parameters but often produce less accurate

predictions. To better represent the complexity of

the data, a deep neural network model is suggested.

To measure the degree of uncertainty surrounding

categorical predictions, two uncertainty measures

are also offered.

of the United States Department of Agriculture

(USDA) uses High-Order Markov

Chains (HOMC) to analyze crop rotation patterns

over time and project future crop-specific

planting. However, HOMCs often face issues with

sparsity and identifiability due to the representation

of categorical data as indicator variables. As the

number of HOMCs needed for analysis increases, the

parametric space's dimension grows exponentially.

Parsimonious representations reduce the number

of parameters but often produce less accurate

predictions. To better represent the complexity of

the data, a deep neural network model is suggested.

To measure the degree of uncertainty surrounding

categorical predictions, two uncertainty measures

are also offered.

Categorical prediction

Neural networks

Uncertainty quantification Introduction

Neural networks

Uncertainty quantification Introduction

Practice and Applications

Symposium on Data Science and Statistics (SDSS) 2024