5 Asked reverse formulation of question where one hopes to find solutions for jump probability assignments from mean occupancy values. Original question is preserved below new formulation.; edited body

# MeanoccupancyFindingjumpprobabilitiesfrommean-occupancyvalues for positionson a one-dimensional random walkwithafinitesetofjumpprobabilities

For each position in the walk, we assign one of $N$ jump probabilities (forward, $N \leq L$) is assigned (forward - $p_k$, backward , - $(1-p_k)$) from a set $P$, where $(p_1, p_2, ..., p_N) \in P$.

For the duration of the random walk, until the absorbing target $x_L$ is reachedHowever, what we do not have knowledge about these assignments. All we are provided with is the mean occupancy of the a given position in the one-dimensional lattice, $x_k$? I'm hoping to find an efficient method to compute an exact solution.

The 'reverse' question may also be interesting -

Let set $M$, $(m_1, m_2, ..., m_L) \in M$be the set , of mean occupancy values for each position in the one-dimensional lattice, $(x_0, x_1, ..., x_L) \in L$. Is there an efficient method

Now, provided access to uniquely reconstruct $M$, to what extent can we find the values for the set of jump probabilities, $(p_1, p_2, ..., p_N) \in P$ (as defined above), for each position in the lattice, $x_k$? Can we guarantee a unique solution by placing certain restrictions on the finite set of jump probabilities $P$?

(Note - This is the reverse formulation of an earlier question I asked about computing mean occupancy for sites in the one-dimensional random walk from assigned jump probabilities. See below for the earlier question.)

Please imagine a discrete random walk on a one-dimensional lattice. The lattice consists of a set of $L$ positions, $(x_0, x_1, ..., x_L) \in L$, where $x_0$ is the initial position of the walk (as well as a reflecting boundary), and $x_L$ is absorbing.

For each position in the walk, we assign one of $N$ jump probabilities (forward, $p_k$, backward, $(1-p_k)$) from a set $P$, where $(p_1, p_2, ..., p_N) \in P$.

For the duration of the random walk, until the absorbing target $x_L$ is reached, what is the mean occupancy of the a given position in the one-dimensional lattice, $x_k$? I'm hoping to find an efficient method to compute an exact solution.

4 Added question about finding jump probabilities from mean occupancy data; deleted 2 characters in body; edited body; added 15 characters in body

Please imagine a discrete random walk on a one-dimensional lattice. The lattice consists of a set of $L$ positions, $(x_0, x_1, ..., x_L) \in L$, where $x_0$ is the initial position of the walk (as well as a reflecting boundary), and $x_L$ is absorbing.

For each position in the walk, we assign one of $N$ jump probabilities (forward, $p_k$, backward, $(1-p_k)$) from a set $P$, where $(p_1, p_2, ..., p_N) \in P$.

For the duration of the random walk, until the absorbing target $x_L$ is reached, what is the mean occupancy of the a given position in the one-dimensional lattice, $x_k$? I'm hoping to find an efficient method to compute an exact solution.

The 'reverse' question may also be interesting -

Let $M$, $(m_1, m_2, ..., m_L) \in M$ be the set of mean occupancy values for each position in the one-dimensional lattice, $(x_0, x_1, ..., x_L) \in L$. Is there an efficient method to uniquely reconstruct the values for the set of jump probabilities, $(p_1, p_2, ..., p_N) \in P$ (as defined above), for each position in the lattice, $x_k$?

3 added 70 characters in body

Please imagine a discrete random walk on a one-dimensional lattice. The lattice consists of a set of $L$ positions, $(x_0, x_1, ..., x_L) \in L$, where $x_0$ is the initial position of the walk (as well as a reflecting boundary), and $x_L$ is absorbing.

For each position in the walk, we assign one of $N$ jump probabilities (forward, $p_k$, backward, $(1-p_k)$) from a set $P$, where $(p_1, p_2, ..., p_N) \in P$.

For the duration of the random walk, until the absorbing target $x_L$ is reached, what is the mean occupancy of the a given position in the one-dimensional lattice, $x_k$? I'm hoping to find an efficient method to compute an exact solution.

2 Changed 'randomly assigned' to 'assigned' (see Steve Huntsman's comment & my reply)
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