Bayesian Parameter Inference for Partially Observed Stopped Processes

Bayesian Parameter Inference for Partially Observed Stopped Processes

Ajay Jasra and Nikolas Kantas

In this article we consider Bayesian parameter inference associated to partially-observed stochastic processes that start from a set and are stopped or killed at the first hitting time of a known set . Such processes occur naturally within the context of a wide variety of applications. The associated posterior distributions are highly complex and posterior parameter inference requires the use of advanced Markov chain Monte Carlo (MCMC) techniques. Our approach uses a recently introduced simulation methodology, particle Markov chain Monte Carlo (PMCMC) [1], where sequential Monte Carlo (SMC) [18, 27] approximations are embedded within MCMC. However, when the parameter of interest is fixed, standard SMC algorithms are not always appropriate for many stopped processes. In [11, 15] the authors introduce SMC approximations of multi-level Feynman-Kac formulae, which can lead to more efficient algorithms. This is achieved by devising a sequence of nested sets from to and then perform the resampling step only when the samples of the process reach intermediate level sets in the sequence. Naturally, the choice of the intermediate level sets is critical to the performance of such a scheme. In this paper, we demonstrate that multi-level SMC algorithms can be used as a proposal in PMCMC. In addition, we propose a flexible strategy that adapts the level sets for different parameter proposals. Our methodology is illustrated on the coalescent model with migration.
Key-Words: Stopped Processes, Sequential Monte Carlo, Markov chain Monte Carlo

Department of Statistics & Applied Probability, National University of Singapore, Singapore, 117546, Sg.


Department of Electrical Engineering, Imperial College London, London, SW7 2AZ, UK.


1 Introduction

In this article we consider Markov processes that are stopped when reaching the boundary of a given set . These processes appear in a wide range of applications, such as population genetics [24, 14], finance [7], neuroscience [5], physics [17, 22] and engineering [6, 26]. The vast majority of the papers in the literature deal with fully observed stopped processes and assume the parameters of the model are known. In this paper we address problems when this is not the case. In particular, Bayesian inference for the model parameters is considered, when the stopped process is observed indirectly via data. We will propose a generic simulation method that can cope with many types of partial observations. To the best of our knowledge, there is no previous work in this direction. An exception is [5], where maximum likelihood inference for the model parameters is investigated for the fully observed case.

In the fully observed case, stopped processes have been studied predominantly in the area of rare event simulation. In order to estimate the probability of rare events related to stopped processes, one needs to efficiently sample realisations of a process that starts in a set and terminates in the given rare target set before returning to or getting trapped in some absorbing set. This is usually achieved using Importance Sampling (IS) or multi-level splitting; see[19, 30] and the references in those articles for an overview. Recently, sequential Monte Carlo (SMC) methods based on both these techniques have been used in [6, 17, 22]. In [10] the authors also prove under mild conditions that SMC can achieve same performance as popular competing methods based on traditional splitting.

Sequential Monte Carlo methods can be described as a collection of techniques used to approximate a sequence of distributions whose densities are known point-wise up to a normalizing constant and are of increasing dimension. SMC methods combine importance sampling and resampling to approximate distributions. The idea is to introduce a sequence of proposal densities and to sequentially simulate a collection of samples, termed particles, in parallel from these proposals. The success of SMC lies in incorporating a resampling operation to control the variance of the importance weights, whose value would otherwise increase exponentially as the target sequence progresses e.g. [18, 27].

Applying SMC in the context of fully observed stopped processes requires using resampling while taking into account how close a sample is to the target set. That is, it is possible that particles close to are likely to have very small weights, whereas particles closer to the starting set can have very high weights. As a result, the diversity of particles approximating longer paths before reaching would be depleted by successive resampling steps. In population genetics, for the coalescent model [24], this has been noted as early as in the discussion of [31] by the authors of [11]. Later, in [11] the authors used ideas from splitting and proposed to perform the resampling step only when each sample of the process reaches intermediate level sets, which define a sequence of nested sets from to . The same idea appeared in parallel in [15, Section 12.2], where it was formally interpreted as an interacting particle approximation of appropriate multi-level Feynman-Kac formulae. Naturally, the choice of the intermediate level sets is critical to the performance of such a scheme. That is, the levels should be set in a “direction” towards the set and so that each level can be reached from the previous one with some reasonable probability [19]. This is usually achieved heuristically using trial simulation runs. Also more systematic techniques exist: for cases where large deviations can be applied in [13] the authors use optimal control and in [8, 9] the level sets are computed adaptively on the fly using the simulated paths of the process.

The contribution of this paper is to address the issue of inferring the parameters of the law of the stopped Markov process, when the process itself is a latent process and is only partially observed via some given dataset. In the context of Bayesian inference one often needs to sample from the posterior density of the model parameters, which can be very complex. Employing standard Markov chain Monte Carlo (MCMC) methods is not feasible, given the difficulty one faces to sample trajectories of the stopped process. In addition, using SMC for sequential parameter inference has been notoriously difficult; see [2, 23]. In particular, due to the successive resampling steps the simulated past of the path of each particle will be very similar to each other. This has been a long standing bottleneck when static parameters are estimated online using SMC methods by augmenting them with the latent state. These issues have motivated the recently introduced particle Markov chain Monte Carlo (PMCMC) [1]. Essentially, the method constructs a Markov chain on an extended state-space in the spirit of [3], such that one may apply SMC updates for a latent process, i.e. use SMC approximations within MCMC. In the context of parameter inference for stopped process this brings up the possibility of using the multi-level SMC methodology as a proposal in MCMC. This idea to the best of our knowledge has not appeared previously in the literature. The main contributions made in this article are as follows:

  • When the sequence of level sets is fixed a priori, the validity of using multi-level SMC within PMCMC is verified.

  • To enhance performance we propose a flexible scheme where the level sets are adapted to the current parameter sample. The method is shown to produce unbiased samples from the target posterior density. In addition, we show both theoretically and via numerical examples how the mixing of the PMCMC algorithm is improved when this adaptive strategy is adopted.

This article is structured as follows: in Section 2 we formulate the problem and present the coalescent as a motivating example. In Section 3 we present multi-level SMC for stopped processes. In Section 4 we detail a PMCMC algorithm which uses multi-level SMC approximations within MCMC. In addition, specific adaptive strategies for the levels are proposed, which are motivated by some theoretical results that link the convergence rate of the PMCMC algorithm to the properties of multi-level SMC approximations. In Section 5 some numerical experiments for the the coalescent are given. The paper is concluded in Section 6. The proofs of our theoretical results can be found in the appendix.

1.1 Notations

The following notations will be used. A measurable space is written as , with the class of probability measures on written . For , the Borel sets are . For a probability measure we will denote the density with respect to an appropriate -finite measure as . The total variation distance between two probability measures is written as . For a vector , the compact notation is used; if is a null vector. For a vector , is the norm. The convention is adopted. Also, is denoted as and is the indicator of a set . Let be a countable (possibly infinite dimensional) state-space, then

denotes the class of stochastic matrices which possess a stationary distribution. In addition, we will denote as the -dimensional vector whose element is and is everywhere else. Finally, for the discrete collection of integers we will use the notation .

2 Problem Formulation

2.1 Preliminaries

Let be a parameter vector on , with an associated prior . The stopped process is a valued discrete-time Markov process defined on a probability space , where is a probability measure defined for every such that for every , is measurable. For simplicity will we will assume throughout the paper that the Markov process is homogeneous. The state of the process begins its evolution in a non empty set obeying an initial distribution and a Markov transition kernel . The process is killed once it reaches a non-empty target set such that . The associated stopping time is defined as

where it is assumed that and , where is a collection of positive integer values related to possible stopping times.

In this paper we assume that we have no direct access to the state of the process. Instead the evolution of the state of the process generates a random observations’ vector, which we will denote as . The realisation of this observations’ vector is denoted as and assume that it takes value in some non empty set . We will also assume that there is no restriction on depending on the observed data , but to simplify exposition this will be omitted from the notation.

In the context of Bayesian inference we are interested in the posterior distribution:


where is the stopping time, is the prior distribution and is the un-normalised complete-data likelihood with the normalising constant of this quantity being:

The subscript on will be used throughout to explicitly denote the conditional dependance on the parameter . Given the specific structure of the stopped processes one may write as


where is the likelihood of the data given the trajectory of the process. Throughout, it will be assumed that for any , , admits a density with respect to a finite measure on and the posterior and prior distributions admit densities respectively both defined with respect to appropriate finite dominating measures.

Note that (1) is expressed as an inference problem for and not only . The overall motivation originates from being able to design a MCMC that can sample from , which requires one to write the target (or an unbiased estimate of it) up-to a normalizing constant [3]. Still, our primary interest lies in Bayesian inference for the parameter and this can be recovered by the marginals of with respect to . As it will become clear in Section 4 the numerical overhead when augmenting the argument of the posterior is necessary and we believe that the marginals with respect to might also be useful by-products.

2.2 Motivating example: the coalescent

The framework presented so far is rather abstract, so we introduce the coalescent model as a motivating example. In Figure 1 we present a particular realisation of the coalescent for two genetic types . The process starts at epoch when the most recent common ancestor (MRCA) splits into two versions of itself. In this example is chosen to be the MCRA and the process continues to evolve by split and mutation moves. At the stopping point (here ) we observe some data , which corresponds to the number of genes for each genetic type.

stop mutationsplitmutationsplit
Figure 1: Coalescent model example: each of denote the possible genetic type of observed chromosomes. In this example we have and . The tree propagates forward in time form the MRCA downwards by a sequence of split and mutation moves. Arrows denote a mutation of one type of a chromosome to another. The name of the process originates from viewing the tree backwards in time (from bottom to top) where the points where the graph join are coalescent events.

In general we will assume there are different genetic types. The latent state of the process is composed of the number of genes of each type at epoch of the process and let also . The process begins by default when the first split occurs, so the Markov chain is initialised by the density

and is propagated using the following transition density:

where is defined in Section 1.1. Here the first transition type corresponds to individuals changing type and is called mutation, e.g. at in Figure 1. The second transition is called a split event, e.g. in the example of Figure 1. To avoid any confusion we stress that in Figure 1 we present a particular realisation of the process that is composed by a sequence of alternate split and mutations, but this is not the only possible sequence. For example, the bottom of the tree could have be obtained with being the possible MCRA and a sequence of two consecutive splits and a mutation.

The process is stopped at epoch when the number of individuals in the population reaches . So for the state space we define:

and for the initial and terminal sets we have:

The data is generated by setting , which corresponds to the counts of genes that have been observed. In the example of Figure 1 this corresponds to . Hence for the complete likelihood we have:


As expected, the density is only non-zero if at time matches the data exactly.

Our objective is to infer the genetic parameters , where and and hence the parameter space can be written as . To facilitate Monte Carlo inference, one can reverse the time parameter and simulate backward from the data. This is now detailed in the context of importance sampling following the approach in [21].

2.2.1 Importance sampling for the coalescent model

To sample realisations of the process for a given , importance sampling is adopted but with time reversed. First we introduce a time reversed Markov kernel with density . This is used as an importance sampling proposal where sampling is performed backwards in time and the weighting forward in time. We initialise using the data and simulate the coalescent tree backward in time until two individuals remain of the same type. This procedure ensures that the data is hit when the tree is considered forward in time.

The process defined backward in time can be interpreted as a stopped Markov process with the definitions of the initial and terminal sets appropriately modified. For convenience we will consider the reverse event sequence of the previous section, i.e we posed the problem backwards in time with the reverse index being . The proposal density for the full path starting from the bottom of the tree and stopping at its root can be written as

With reference to (3) we have

Then the marginal likelihood can be obtained

In [31] the authors derive an optimal proposal with respect to the variance of the marginal likelihood estimator. For the sake of brevity we omit any further details. In the current setup where there is only mutation and coalescences, the stopped-process can be integrated out [20], but this is not typically possible in more complex scenarios. A more complicated problem, including migration, is presented in Section 5.2. Finally, we remark that the relevance of the marginal likelihood above will become clear later in Section 4 as a crucial element in numerical algorithms for inferring .

3 Multi-Level Sequential Monte Carlo Methods

In this section we shall briefly introduce generic SMC without extensive details. We refer the reader for a more detailed description to [15, 18]. To ease exposition, when presenting generic SMC, we shall drop the dependence upon parameter .

SMC algorithms are designed to simulate from a sequence of probability distributions defined on state space of increasing dimension, namely . Each distribution in the sequence is assumed to possess densities with respect to a common dominating measure:

with each un-normalised density being and the normalizing constant being . We will assume throughout the article that there are natural choices for and that we can evaluate each point-wise. In addition, we do not require knowledge of

3.1 Generic SMC algorithm

SMC algorithms approximate recursively by propagating a collection of properly weighted samples, called particles, using a combination of importance sampling and resampling steps. For the importance sampling part of the algorithm, at each step of the algorithm, we will use general proposal kernels with densities , which possess normalizing constants that do not depend on the simulated paths. A typical SMC algorithm is given in Algorithm 1 and we obtain the following SMC approximations for

and for the normalizing constant :


Initialisation, :


  1. Sample .

  2. Compute weights

For ,

For ,

  1. Resampling: sample index , where .

  2. Sample and set .

  3. Compute weights

Algorithm 1 Generic SMC Algorithm

In this paper we will use to be the multinomial distribution. Then the resampled index of the ancestor of particle at time , namely , is also a random variable with value chosen with probability . For each time , we will denote the complete collection of ancestors obtained from the resampling step as and the randomly simulated values of the state obtained during sampling (step 2 for ) as . We will also denote the concatenated vector of all these variables obtained during the simulations from time . Note the is a vector containing all simulated states and should not be confused with the particle sample of the path .

Furthermore, the joint density of all the sampled particles and the resampled indices is


The complete ancestral genealogy at each time can always traced back by defining an ancestry sequence for every and . In particular, we set the elements of using the backward recursion where . In this context one can view SMC approximations as random probability measures induced by the imputed random genealogy and all the possible simulated state sequences that can be obtained using . This interpretation of SMC approximations was introduced in [1] and will be later used together with for establishing the complex extended target distribution of PMCMC.

3.2 Multi-Level SMC implementation

For different classes of problems one can find a variety of enhanced SMC algorithms; see e.g. [18]. In the context of stopped processes, a multi-level SMC implementation was proposed in [11] and the approach was illustrated for the coalescent model of Section 2.2. We consider a modified approach along the lines of Section 12.2 of [15] which seems better suited for general stopped processes and can provably yield estimators of much lower variance relative to vanilla SMC.

Introduce an arbitrary sequence of nested sets

with the corresponding stopping times denoted as

Note that the Markov property of implies .

The implementation of multi-level SMC differs from the generic algorithm of Section 3.1 in that between successive resampling steps one proceeds by propagating in parallel trajectories of until the set is reached for each . For a given the path is “frozen” once , until the remaining particles reach and then a resampling step is performed. More formally denote for

where is a realisation for the stopping time and similarly for we have

Multi-level SMC is a SMC algorithm which ultimately targets a sequence of distributions each defined on a space


where , and are finite collections of positive integer values related to the stopping times respectively. In the spirit of generic SMC define intermediate target densities w.r.t to an appropriate -finite dominating measure . We will assume there exists a natural sequence of densities obeying the restriction so that the last target density coincides with in (2). Note that we define a sequence of target densities, but this time the dimension of compared to grows with a random increment of . In addition, should clearly depend on the value of , but this suppressed in the notation. The following proposition is a direct consequence of the Markov property:

Proposition 3.1.

Assume . Then the stochastic sequence defined forms a Markov chain taking values in defined (6). In addition, for any bounded measurable function , then .

The proof can be found in [15, Proposition 12.2.2, page 438], [15, Proposition 12.2.4, page 444] and the second part is due to .

We will present multi-level SMC based as a particular implementation of the generic SMC algorithm. Firstly we replace with respectively. Contrary to the presentation of Algorithm 1 for multi-level SMC we will use a homogeneous Markov importance sampling kernel , where , by convention and is the corresponding density w.r.t. . To compute the importance sampling weights of step 3 for in Algorithm 1 we use instead:

and for step 2 at :

To simplify notation from herein we write

and given , for any we have

where again we have suppressed the -dependance of in the notation. We present the multi-level SMC algorithm in Algorithm 2. Note here we include a procedure whereby at each stage , particles that do not reach before time are rejected by assigning them a zero weight, whereas before it was hinted that resampling is performed when all particles reach . Similar to (5), it is clear that the joint probability density of all the random variables used to implement a multi-level SMC algorithm with multinomial resampling is given by:


where is defined similarly to . Finally, recall by construction so the approximation of the normalizing constant of for a fixed is


Initialisation, :


  1. For :

    1. Sample .

    2. If set , and go to step 2.

  2. Compute weights

For ,

For ,

  1. Resampling: sample index , where .

  2. For :

    1. Sample .

    2. If set , and go to step 3.

  3. Set .

  4. Compute weights

Algorithm 2 Multi-level SMC Algorithm

3.2.1 Setting the levels

We will begin by showing how the levels can be set for the coalescent example of Section 2.2. We will proceed in the spirit of Section 2.2.1 and consider the backward process so that the “time” indexing is set to start from the bottom of the tree towards the root. We introduce a a collection of integers and define

Clearly we have , and . One can also write the sequence of target densities for the multi-level setting as:

The major design problem that remains in general is that given any candidates for , how to set the spacing (in some sense) of the and how many levels are needed so that good SMC algorithms can be constructed. That is, if the are far apart, then one can expect that weights will degenerate very quickly and if the are too close that the algorithm will resample too often and hence lead to poor estimates. For instance, in the context of the coalescent example of Section 2.2, if one uses the above construction for the importance weight at the -th resampling time is

Now, in general for any and it is hard to know beforehand how much better (or not) the resulting multi-level algorithm will perform relative to a vanilla SMC algorithm. Whilst [11] show empirically that in most cases one should expect a considerable improvement, there is considered to be fixed. In this case one could design the levels sensibly using offline heuristics or more advanced systematic methods using optimal control [13] or adaptive simulation [8, 9], e.g. by setting the next level using the median of a pre-specified rank of the particle sample. What we aim to establish in the next section is that when is varies as in the context of MCMC algorithms, one can both construct PMCMC algorithms based on multi-level SMC and more importantly easily design for each different sequences for based on similar ideas.

4 Multi-Level Particle Markov Chain Monte Carlo

Particle Markov Chain Monte Carlo (PMCMC) methods are MCMC algorithms, which use all the random variables generated by SMC approximations as proposals. As in standard MCMC the idea is to run an ergodic Markov chain to obtain samples from the distribution of interest. The difference lies that in order use the simulated variables from SMC, one defines a complex invariant distribution for the MCMC on an extended state space. This extended target is such that a marginal of this invariant distribution is the one of interest.

This section aims on providing insight to the following questions:

  1. Is it valid in general to use multi-level SMC within PMCMC?

  2. Given that it is, how can we use the levels to improve the mixing of PMCMC?

The answer to the first question seems rather obvious, so we will provide some standard but rather strong conditions for which multi-level PMCMC is valid. For the second question we will propose an extension to PMMH that adapts the level sets used to at every iteration of PMCMC. [1] introduces three different and generic PMCMC algorithms: particle independent Metropolis Hastings algorithm (PIMH), particle marginal Metropolis Hastings (PMMH) and particle Gibbs samplers. In the remainder of the paper we will only focus on the first two of these.

4.1 Particle independent Metropolis Hastings (PIMH)

  1. Sample from (7) using the multi-level implementation of Algorithm 1 detailed in Section (3.2) and compute . Sample .

  2. Set and

  3. For :

    1. Propose a new and as in step 1 and compute

    2. Accept this as the new state of the chain with probability If we accept, set and . Otherwise reject, and .

Algorithm 3 Particle independent Metropolis-algorithm (PIMH)

We will begin by presenting the simplest generic algorithm found in [1], namely the particle independent Metropolis Hastings algorithm (PIMH). In this case and are fixed and PIMH is designed to sample from the pre-specified target distribution also considered in Section 3.1. Although PIMH is not useful for parameter inference it is included for pedagogic purposes. One must bear in mind that PIMH is the most basic of all PMCMC algorithms. As such it is easier to analyse but still can provide useful intuition that can be used later in the context of PMMH and varying .

PIMH is presented in Algorithm 3. It can be shown, using similar arguments to [1], that the invariant density of the Markov kernel above is exactly (see the proof of Proposition 4.2)

where is as in (7) and as before we have and for every . Note that admits the target density of interest, as the marginal, when and are integrated out.

We commence by briefly investigating some convergence properties of PIMH with multi-level SMC. Even though the scope of PIMH is not parameter inference, one can use insight on what properties are desired by multi-level SMC for PIMH when designing other PMCMC algorithms used for parameter inference. We begin with posing the following mixing and regularity assumption:

  • For every and there exist a such that for every :

    There exist a such that for and every :