CS 7638 MIDTERM REVIEW FLASHCARDS 2025/26 QUESTIONS & ANSWERS RATED 100%
CORRECT.
Time & Space Complexity for Histogram Filters - (ANSWER)Exponential - O(n^m), where n is the
resolution of our bins and m is our number of dimensions.
Bayes' Rule - (ANSWER)P(A|B)=P(B|A)P(A)/P(B)
Properties of Kalman Filters - (ANSWER)Continuous, Unimodal, Symmetric, Approximate
Properties of Particle Filters - (ANSWER)Continuous, Multimodal, Approximate
Properties of Histogram Filters - (ANSWER)Discrete, Multimodal, Approximate
Time & Space Complexity of Particle Filters - (ANSWER)??? - Can be exponential, can be better in some
applications.
Time & Space Complexity of Kalman Filters - (ANSWER)Quadratic - O(N^2)?
When do we use convolutions in the motion/measure cycle? - (ANSWER)Motion
When do we use multiplication in the motion/measure cycle? - (ANSWER)Measurement
When do we use the Law of Total Probability in the motion/measure cycle? - (ANSWER)Motion
When do we use Bayes' Rule in the motion/measure cycle? - (ANSWER)Measurement
In Kalman Filters, what is x? - (ANSWER)The filter's current state estimate/prediction.
In Kalman Filters, what is F? - (ANSWER)The state dynamics/state transition model.
In Kalman Filters, what is P? - (ANSWER)The prediction uncertainty of the Kalman Filter.
CORRECT.
Time & Space Complexity for Histogram Filters - (ANSWER)Exponential - O(n^m), where n is the
resolution of our bins and m is our number of dimensions.
Bayes' Rule - (ANSWER)P(A|B)=P(B|A)P(A)/P(B)
Properties of Kalman Filters - (ANSWER)Continuous, Unimodal, Symmetric, Approximate
Properties of Particle Filters - (ANSWER)Continuous, Multimodal, Approximate
Properties of Histogram Filters - (ANSWER)Discrete, Multimodal, Approximate
Time & Space Complexity of Particle Filters - (ANSWER)??? - Can be exponential, can be better in some
applications.
Time & Space Complexity of Kalman Filters - (ANSWER)Quadratic - O(N^2)?
When do we use convolutions in the motion/measure cycle? - (ANSWER)Motion
When do we use multiplication in the motion/measure cycle? - (ANSWER)Measurement
When do we use the Law of Total Probability in the motion/measure cycle? - (ANSWER)Motion
When do we use Bayes' Rule in the motion/measure cycle? - (ANSWER)Measurement
In Kalman Filters, what is x? - (ANSWER)The filter's current state estimate/prediction.
In Kalman Filters, what is F? - (ANSWER)The state dynamics/state transition model.
In Kalman Filters, what is P? - (ANSWER)The prediction uncertainty of the Kalman Filter.