MCMC non-convergence and catastrophic predictive_accuracy on 1.6.0 with official tfp-nightly dependency

### Summary

Upgrading from `google-meridian==1.4.0` to `google-meridian==1.6.0` with the **official** dependency stack from [pyproject.toml](https://github.com/google/meridian/blob/v1.6.0/pyproject.toml) (`tfp-nightly==0.26.0.dev20260130`) causes complete MCMC non-convergence and nonsensical goodness-of-fit metrics, while the same training data, config, and priors work on 1.4.0.

### Environment

- Platform: linux aarch64 (Docker on Apple Silicon)
- Python: 3.11
- `google-meridian`: 1.6.0
- `tensorflow`: 2.20.0
- `tfp-nightly`: 0.26.0.dev20260130 (as required by pyproject.toml)
- `numpy`: 2.3.5 (< 2.4)

Working baseline (1.4.0):

- `google-meridian`: 1.4.0
- `tensorflow`: 2.20.0
- `tensorflow-probability`: 0.25.0 (stable; declared in [v1.4.0 pyproject.toml](https://github.com/google/meridian/blob/v1.4.0/pyproject.toml))

### Reproduction

National/geo MMM, `kpi_type=non_revenue`, no `revenue_per_kpi`, ROI priors via `PriorDistribution(roi_m=LogNormal(...))`, holdout_id mask, MCMC: 8 chains / 2000 adapt / 1000 burnin / 2000 keep.

Install official 1.6.0 stack:

```bash
pip install 'numpy<2.4' 'google-meridian==1.6.0'
pip freeze | grep -iE 'meridian|tensorflow|tfp'
```

Train model, then:

```python
from meridian.analysis import analyzer
from meridian.analysis.review import reviewer

analysis = analyzer.Analyzer(mmm)
print(analysis.predictive_accuracy(use_kpi=True).to_dataframe())

health = reviewer.ModelReviewer(mmm).run()
print(health.overall_status, health.health_score, health.summary_message)
```

### Actual results (1.6.0 + official tfp-nightly)

**Predictive accuracy (ALL geo model):**

| metric | geo_granularity | evaluation_set | value |
|--------|-----------------|----------------|-------|
| R_Squared | national | All Data | -68817.18 |
| MAPE | national | All Data | 62.83 |
| wMAPE | national | All Data | 64.02 |

**Health check:**

- `overall_status`: FAIL
- `health_score`: 0.0
- `max_r_hat` for `roi_m`: **338660163584.00**
- Message: "Failed: Model did not converge. Other checks were skipped."

### Expected results (1.4.0 + tensorflow-probability 0.25.0)

Same data and config on 1.4.0:

| metric | geo_granularity | evaluation_set | value |
|--------|-----------------|----------------|-------|
| R_Squared | national | All Data | 0.908 |
| MAPE | national | All Data | 0.051 |
| wMAPE | national | All Data | 0.053 |

### Notes

1. `tfp-nightly` is an **explicit** dependency in 1.5.3+ pyproject.toml, not an accidental resolver artifact.
2. 1.5.3 also declares the same `tfp-nightly` pin; we have not fully validated whether 1.5.3 official stack reproduces the issue.
3. Replacing `tfp-nightly` with `tensorflow-probability==0.25.0` after installing 1.6.0 is a possible workaround but violates declared dependencies and may break if 1.6.0 calls 0.26-only APIs.

Start by comparing the v1.6.0 and v1.4.0 pyproject.toml dependency declarations, then reproduce the reported training setup with the official v1.6.0 stack. Inspect analyzer.Analyzer.predictive_accuracy and reviewer.ModelReviewer outputs alongside MCMC convergence; done means the official dependency configuration no longer produces the reported non-convergence and catastrophic metrics.

Written by the indexing model from the issue text.