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It is possible to do that using the algorithm known as the Independent Metropolis-Hastings sampler without having to do any transformation and without having to compute the constant normalizing terms.

Assume you can sample from the density $q(x)\propto \exp(g(x))$ where $g(x)$ is a polynomial (say, you are sampling from the normal distribution) and your objective is to get a sample from the density $p(x)\propto \exp(f(x))$ where $f(x)$ is another polynomial (naturally both polynomials need to have negative leading term to insure those densities exist). Then the algorithm will produce a Markov process whose invariant distribution is $p(x)$. Starting from some arbitrary initial value, say $X^0$, let's say that you have already $M$ draws. To get a new sample draw $X^{M+1}$, draw a random variable from $q$. Let's call that draw $X'$. Then accept or rejet $X'$ with probability $$1 \wedge \exp[f(X')-f(X^M)+g(X^M)-g(X')]$$ If you have accepted then set $X^{M+1}=X'$ otherwise set $X^{M+1}=X^M$.

Eventually that Markov process would have converged and you could consider say the last $N$ draws as such a sample from the desired distribution.

A very good introduction to that algorithm is section 7.4 of this excellent book

Edit As indicated by the author of the question in a comment below, if the question is to transform a sample from one continuous distribution having density $q(x)$ into a sample from another continuous distribution having density $p(x)$, then this could be achieved in the following way.

Let $Q$ and $P$ be respectively the CDF's with associated with $q$ and $p$. p$ respectively. Assume we have an i.i.d. sample from $X_1$,...,$X_n$ from $q$, then $P^{-1}(Q(X_1))$,...,$P^{-1}(Q(X_n))$ is an i.i.d. sample from $p$. (this is straightforward to prove).

Now, the issue in your case is that $P$ or and the quantile function $P^{-1}$ may not be known in closed-form (even the normalizing constant of the density associated with it $P$ may be not be known in closed form.) However, numerical integration and interpolation could work here.
show/hide this revision's text 3 edited body; deleted 23 characters in body; added 66 characters in body

It is possible to do that using the algorithm known as the Independent Metropolis-Hastings sampler without having to do any transformation and without having to compute the constant normalizing terms.

Assume you can sample from the density $q(x)\propto \exp(g(x))$ where $g(x)$ is a polynomial (say, you are sampling from the normal distribution) and your objective is to get a sample from the density $p(x)\propto \exp(f(x))$ where $f(x)$ is another polynomial (naturally both polynomials need to have negative leading term to insure those densities exist). Then the algorithm will produce a Markov process whose invariant distribution is $p(x)$. Starting from some arbitrary initial value, say $X^0$, let's say that you have already $M$ draws. To get a new sample draw $X^{M+1}$, draw a random variable from $q$. Let's call that draw $X'$. Then accept or rejet $X'$ with probability $$1 \wedge \exp[f(X')-f(X^M)+g(X^M)-g(X')]$$ If you have accepted then set $X^{M+1}=X'$ otherwise set $X^{M+1}=X^M$.

Eventually that Markov process would have converged and you could consider say the last $N$ draws as such a sample from the desired distribution.

A very good introduction to that algorithm is section 7.4 of this excellent book

Edit As indicated by the author of the question in a comment below, if the question is to transform a sample from one continuous distribution having density $q(x)$ into a sample from another continuous distribution having density $p(x)$, then this could be achieved in the following way.

Let $Q$ and $P$ be respectively the CDF's with associated with $q$ and $p$. Assume we have an i.i.d. sample from $X_1$,...,$X_n$ from $q$, then $P(Q^{-1}(X_1))$,...,$P(Q^{-1}(X_n))$ P^{-1}(Q(X_1))$,...,$P^{-1}(Q(X_n))$ is an i.i.d. sample from $p$. (this is straightforward to prove).

Now, the issue in your case is that $P$ or the quantile function may not be known in closed-form (even the normalizing constant of the density associated with it may be not be known in closed form.) However, numerical integration and interpolation could work here.
show/hide this revision's text 2 added 786 characters in body

It is possible to do that using the algorithm known as the Independent Metropolis-Hastings sampler without having to do any transformation and without having to compute the constant normalizing terms.

Assume you can sample from the density $q(x)\propto \exp(g(x))$ where $g(x)$ is a polynomial (say, you are sampling from the normal distribution) and your objective is to get a sample from the density $p(x)\propto \exp(f(x))$ where $f(x)$ is another polynomial (naturally both polynomials need to have negative leading term to insure those densities exist). Then the algorithm will produce a Markov process whose invariant distribution is $p(x)$. Starting from some arbitrary initial value, say $X^0$, let's say that you have already $M$ draws. To get a new sample draw $X^{M+1}$, draw a random variable from $q$. Let's call that draw $X'$. Then accept or rejet $X'$ with probability $$1 \wedge \exp[f(X')-f(X^M)+g(X^M)-g(X')]$$ If you have accepted then set $X^{M+1}=X'$ otherwise set $X^{M+1}=X^M$.

Eventually that Markov process would have converged and you could consider say the last $N$ draws as such a sample from the desired distribution.

A very good introduction to that algorithm is section 7.4 of this excellent book

Edit As indicated by the author of the question in a comment below, if the question is to transform a sample from one continuous distribution having density $q(x)$ into a sample from another continuous distribution having density $p(x)$, then this could be achieved in the following way.

Let $Q$ and $P$ be respectively the CDF's with associated with $q$ and $p$. Assume we have an i.i.d. sample from $X_1$,...,$X_n$ from $q$, then $P(Q^{-1}(X_1))$,...,$P(Q^{-1}(X_n))$ is an i.i.d. sample from $p$. (this is straightforward to prove).

Now, the issue in your case is that $P$ may not be known in closed-form (even the normalizing constant of the density associated with it may be not be known in closed form.) However, numerical integration could work here.
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