Undergrad How to find the inverse of an integral transform?

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To find the distribution of a random variable T supported on [t1, t2], the integral equation involving the function V(t', T) must be solved, where V has challenging properties such as being nowhere differentiable for t < t' and having a jump at t = t'. The goal is to determine the inverse of this integral transform to satisfy the expectation condition E[V(t', T)] = K for all t' in the specified range. The discussion seeks methods for finding the inverse transform despite the complexities of the kernel V, including potential numerical techniques. Participants are encouraged to share insights or alternative approaches to solve the equations effectively.
hyurnat4
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I'm trying to find the distribution of a random variable ##T## supported on ##[t_1, t_2]## subject to ## \mathbb{E}[V(t', T)] = K, \forall t' \in [t_1, t_2]##. In integral form, this is : $$ \int_{t_1}^{t_2} V(t', t).f(t) \, dt = K,\forall t' \in [t_1, t_2], $$ which is just an exotic integral transform.

So if I can find the inverse transform, I'm done. But the function ##V## is ...not nice. It's nowhere differentiable for ##t < t'##, there's a jump at ##t = t'## and it's constant from then on.

So do you guys know of any methods to find the inverse to an integral transform with a nasty kernel like ##V##? Or can you see another way to solve these equations? I'd even be happy with numerical techniques.

Thanks in advance.
 
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