Approach

$$$$p(z mid L) == N(r ; hat(r(s, v, L)), sigma**2)

Approach

$$$$p(Z mid L) == prod(p(z_k mid L))

Maximum Likelihood Estimation

$$$$def expectation(l):

N=10000

return mean(l() for _ in range(N))

L__star == operatornamewithlimits(argmax)_L * p(Z mid L) * abs_J(L)

<= J_max(

== operatornamewithlimits(argmin)_L - log(p(Z mid L) /_(J(L)

<= J_max))

== operatornamewithlimits(argmin)_L sum((r_k - hat(r(s_k, v_k,

L))) /_(J(L) <= J_max))

eqqcolon operatornamewithlimits(argmin)_L expectation(lambda:

(Z, L)))

Approach

$$$$p(z mid L) == N(r ; hat(r(s, v, L)), sigma**2)

Approach

$$$$p(Z mid L) == prod(p(z_k mid L))

Maximum Likelihood Estimation

$$$$def expectation(l):

N=10000

return mean(l() for _ in range(N))

L__star == operatornamewithlimits(argmax)_L * p(Z mid L) * abs_J(L)

<= J_max(

== operatornamewithlimits(argmin)_L - log(p(Z mid L) /_(J(L)

<= J_max))

== operatornamewithlimits(argmin)_L sum((r_k - hat(r(s_k, v_k,

L))) /_(J(L) <= J_max))

eqqcolon operatornamewithlimits(argmin)_L expectation(lambda:

(Z, L)))