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HW6: Probabilistic Reasoning (Programming Assignment) | Gradescope

https://www.gradescope.com/courses/361635/assignments/2004636/submissions/new 1/10

0/21 Questions Answered

HW6: Probabilistic Reasoning

(Programming Assignment)

Q1 Particle Filtering

75 Points

Q1.1 Task 1.1

25 Points

Nothing to submit here. Just include your code in the zip file you

submit in Q4. Worth 25 points.

Save Answer

Q1.2 Task 1.2

10 Points

Upload your plots here. Don't cherry-pick. We may run your code to

generate plots and compare. Just upload an image of one or more

plots representing typical performance.

Please select file(s) Select file(s)

Particle count

Enter your answer here

Save Answer

Q1.3 Task 1.3: Accuracy vs noise

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Select all true statements.

Save Answer

Q1.4 Task 1.3: Accuracy vs number of particles

4 Points

Select all true statements.

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Accuracy and noise are directly proportional.

Accuracy and noise are inversely proportional.

Accuracy plateaus as noise increases.

Accuracy plateaus as noise decreases.

Particle filter is accurate with low nonzero noise.

Particle filter is not accurate with low nonzero noise.

There is little to no effect of noise on accuracy.

Accuracy and number of particles are directly proportional.

Accuracy and number of particles are inversely proportional.

Accuracy plateaus as number of particles increases.

Accuracy plateaus as number of particles decreases.

A high number of particles can compensate for a decrease in

accuracy due to noise.

There is little to no effect of number of particles on accuracy.

4/20/22, 9:41 AM Submit HW6: Probabilistic Reasoning (Programming Assignment) | Gradescope

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Q1.5 Task 1.3: Accuracy vs max sensor range

4 Points

Select all true statements.

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Q1.6 Task 1.3: Efficiency vs noise

4 Points

Select all true statements.

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Accuracy and max sensor range are directly proportional.

Accuracy and max sensor range are inversely proportional.

Accuracy plateaus as max sensor range increases.

Accuracy plateaus as max sensor range decreases.

There is little to no effect of max sensor range on accuracy.

Efficiency and noise are directly proportional.

Efficiency and noise are inversely proportional.

Efficiency plateaus as noise increases.

Efficiency plateaus as noise decreases.

Particle filter is efficient with low nonzero noise.

Particle filter is not efficient with low nonzero noise.

There is little to no effect of noise on efficiency.

4/20/22, 9:41 AM Submit HW6: Probabilistic Reasoning (Programming Assignment) | Gradescope

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Q1.7 Task 1.3: Efficiency vs number of particles

4 Points

Select all true statements.

Save Answer

Q1.8 Task 1.3: Efficiency vs max sensor range

4 Points

Select all true statements.

Save Answer

Q1.9 Task 1.4

4 Points

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Efficiency and number of particles are directly proportional.

Efficiency and number of particles are inversely proportional.

Efficiency plateaus as number of particles increases.

Efficiency plateaus as number of particles decreases.

There is little to no effect of number of particles on efficiency.

Efficiency and max sensor range are directly proportional.

Efficiency and max sensor range are inversely proportional.

Efficiency plateaus as max sensor range increases.

Efficiency plateaus as max sensor range decreases.

There is little to no effect of max sensor range on efficiency.

4/20/22, 9:41 AM Submit HW6: Probabilistic Reasoning (Programming Assignment) | Gradescope

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What probabilistic assumptions are we making about the state

transition model? Select all that are true.

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Q1.10 Task 1.4

4 Points

How well do these assumptions match the true state transition model?

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The state transition model is Markovian.

The state transition model is non-Markovian.

The distribution of next state conditioned on previous state is

uniform.

The distribution of next state conditioned on previous state is

Gaussian.

The Markovian assumption is held.

The Markovian assumption is not held.

The non-Markovian assumption is held.

The non-Markovian assumption is not held.

The uniform assumption is held.

The uniform assumption is not held.

The Gaussian assumption is held.

The Gaussian assumption is not held.

4/20/22, 9:41 AM Submit HW6: Probabilistic Reasoning (Programming Assignment) | Gradescope

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Q1.11 Task 1.4

4 Points

In what way and to what extent is performance affected by making

these assumptions?

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Q1.12 Task 1.5

4 Points

Describe some cases in which the particle filter fails to localize the car

in this map, and explain why this happens. Your response should be

less than 5 sentences.

Enter your answer here

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The Markovian assumption has a positive impact on accuracy.

The Markovian assumption has a negative impact on accuracy.

The non-Markovian assumption has a positive impact on

accuracy.

The non-Markovian assumption has a negative impact on

accuracy.

The uniform assumption has a positive impact on accuracy.

The uniform assumption has a negative impact on accuracy.

The Gaussian assumption has a positive impact on accuracy.

The Gaussian assumption has a negative impact on accuracy.

4/20/22, 9:41 AM Submit HW6: Probabilistic Reasoning (Programming Assignment) | Gradescope

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Q2 Bayesian Networks

25 Points

Q2.1 Task 2.1

5 Points

Drawing of the Bayes net structure:

Please select file(s) Select file(s)

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Q2.2 Task 2.2

10 Points

Nothing to submit here. Just include your code in the zip file you

submit in Q4. Worth 10 points.

Save Answer

Q2.3 Task 2.3

5 Points

Optimal overtake condition found by your code:

Save Answer

Q2.4 Task 2.3

5 Points

Mathematical formula:

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?

? MuchFaster = True, Early = True

? MuchFaster = True, Early = False

? MuchFaster = False, Early = True

? MuchFaster = False, Early = False

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Please select file(s) Select file(s)

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Q3 Kalman Filtering (Extra Credit)

0 Points

Q3.1 Task 3.1

0 Points

(+5 points) Nothing to submit here. Just include your code in the zip

file you submit in Q4.

Save Answer

Q3.2 Task 3.2

0 Points

(+5 points) Explain how you set your parameters. Your response must

be no more than 10 sentences.

Enter your answer here

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Q3.3 Task 3.3

0 Points

(+5 points)

Plot for KF with GPS noise dist = gaussian, GPS gaussian noise var =

10.0

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?

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Plot for KF with GPS noise dist = uniform, GPS uniform noise width =

20.0

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Accuracy vs measurement noise var/width (no more than 3 sentences)

Enter your answer here

Accuracy vs measurement noise distribution (gaussian or uniform) (no

more than 3 sentences)

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Optional: if you want to reference plots or other illustrations in your

responses above, upload them here:

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Q3.4 Task 3.4

0 Points

(+5 points) Your responses must be no more than 3 sentences each.

What assumptions does the Kalman filter make about the GPS

measurements?

Enter your answer here

How well do these assumptions match the process that generates the

data?

? ?

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Enter your answer here

In what way and to what extent is performance affected by these

making these assumptions?

Enter your answer here

Optional: if you want to reference plots or other illustrations in your

responses above, upload them here:

Please select file(s) Select file(s)

Save Answer

Q4 Code submission

0 Points

Submit your zip file here as described in the project PDF:

https://github.com/jdkanu/cmsc421-

p3/blob/main/Programming_Assignment_3__Probabilistic_Reasoning.pdf

Please select file(s) Select file(s)

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