# Exercise 1

Exercise 1: Effect of non-process error (it helps here to think of it as measurement error)

Type 'Lab1' at matlab prompt.

When asked for data code, type 3 (this is a White-capped albatross time series).

Param set 1: set mu = 0; s2p = 0.01; s2np = 0.1

Param set 2: set mu = 0; s2p = 0.01; s2np = 0.01

Param set 3: set mu = 0; s2p = 0.01; s2np = 0.001

Questions for Exercise 1:

1. Why is the fit closer to the observations as the npe parameter is lowered?

2. The ML fit hardly seems to fit the data at all. What's going on there?

Type 'Lab1' at matlab prompt.

When asked for data code, type 3 (this is a White-capped albatross time series).

Param set 1: set mu = 0; s2p = 0.01; s2np = 0.1

Param set 2: set mu = 0; s2p = 0.01; s2np = 0.01

Param set 3: set mu = 0; s2p = 0.01; s2np = 0.001

Questions for Exercise 1:

1. Why is the fit closer to the observations as the npe parameter is lowered?

2. The ML fit hardly seems to fit the data at all. What's going on there?

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